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<> machine learning (526)

Saudi HPC/AI Conference 2026

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KAUST

Join researchers, technology experts and industry leaders to explore how HPC and AI are advancing research and innovation.

Transformers from Compressed Representations

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B9 R2325

This talk explores how transformers can effectively be trained on compressed byte streams in order to gain computational and memory efficiencies.

The Broximal Point Method and Its Impact on Optimization Theory and Machine Learning

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B9 R2325

This talk introduces the Broximal Point Method (BPM), a condition-number-free alternative to classical proximal methods, that replaces proximal penalties with ball constraints, surveys its theoretical extensions and distributed variants, and explains how approximate non-Euclidean Brox updates underpin practical machine learning optimizers.

FOCUS cuts wasted computing in diffusion language models

2 min read ·
KAUST-led research identifies how diffusion language models can avoid unnecessary calculations, increasing throughput by up to 3.52 times while maintaining generation quality.

Muhammet Annayev

"Optimizing the invisible networks of today to secure the hyper-connected world of tomorrow."

Ans Rehman

"Pressure is a privilege." — Billie Jean King

Modeling physical change for more realistic image editing

2 min read ·
A new KAUST-led approach helps AI understand physical change, improving image edits involving light, motion and materials.

Understanding Practical Optimization in Machine Learning

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This dissertation studies how practical optimization methods used to train modern machine learning systems can be better understood by analyzing the objectives, updates, assumptions, and approximations that arise in their actual implementation.

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization

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B5 R5209

This thesis investigates seven critical challenges at the intersection of theory and practice, specifically focusing on the fundamental bottlenecks of Federated Learning and distributed optimization and develops novel algorithmic frameworks that provide sharp theoretical guarantees to bridge the gap between heuristic success and mathematical rigor.

Enabling Seamless Connectivity via Virtualization Technologies in 6G Integrated Networks

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B1 R4214

This thesis investigates how integrated networks powered by such technologies can be designed and optimized to enable seamless connectivity.

Reinforcement Learning and Optimization in Large Action Spaces under Limited Feedback

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B2 R5209

This dissertation develops theoretical foundations and scalable algorithms for reinforcement learning and optimization in large decision spaces under limited feedback.

Protecting the systems that power modern life

4 min read ·
The modern world runs on interconnected cyber-physical and distributed digital systems. When cyber-physical systems are compromised, the consequences can extend into the physical world. A cyberattack on a maritime system can disrupt supply chains. A breach in financial infrastructure can destabilize economies. An autonomous system exploit can paralyze transportation networks. These scenarios raise a fundamental question: how can digital and cyber-physical systems be designed to remain secure and resilient in increasingly complex environments? For three decades, KAUST Professor Roberto Di

Private and Robust Learning for Decision Making

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This thesis develops the theoretical foundations of decision-making under privacy, heavy-tailed feedback, and data contamination, establishing fundamental limits and designing near-optimal algorithms across bandits, reinforcement learning, and RLHF.

Reduced-Order High Fidelity Simulations of Reacting Flows Using Low Dimensional Manifolds and Machine Learning

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B1 R3119

This talk will provide an overview of historical developments in mathematical and computational approaches to reduced order models for accelerated high fidelity reacting flow simulations in modern computing hardware.

Bushra Bin Hemid

M.S. Student
MSc/PhD candidate in Artificial Intelligence at KAUST. Expertise in AI and information security. Research focuses on AI for infrared imaging, transforming sensing technologies for healthcare, agriculture, and smart infrastructure

Echo Ziyi Yang

Ph.D. Student

Provable and Measurable Machine Unlearning in Modern Learning Systems

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B2 L5 R5209

This dissertation examines the foundations of machine unlearning under realistic learning system constraints and proposes both theoretically grounded unlearning algorithms and principled evaluation frameworks for modern learning systems.

Event Status: Cancelled | Remote Sensing and Agroinformatics Insights in Saudi Arabia Using Machine Learning

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B9 L2 R2325

This talk explores how machine learning and high-resolution satellite remote sensing are being used to transform vast amounts of raw data into actionable agroinformatics at a national scale, providing the precision needed to manage these vital resources sustainably.

Ting Li

Postdoctoral Research Fellow, Environmental Science and Engineering
Ting Li is a Postdoctoral Research Fellow focusing her research on bridging the gap between complex data and real-world agricultural impact.

Yanda Meng

Assistant Professor, Bioengineering
Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

Rigorous Model-Constrained Scientific Machine Learning for Digital Twins: A Computational Mathematics Perspective

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B1 L3 R3119

This talk will outline a principled pathway from traditional computational mathematics to rigorously grounded Scientific Machine Learning (SciML) and present recent Scientific Deep Learning (SciDL) methods for forward modeling, inverse and calibration problems, and uncertainty quantification, emphasizing mathematical structure, stability, and generalization.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026

1 min read ·
Join us at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2026 for new research on stochastic algorithms, statistical learning, and optimization, with applications in climate modelling and computational finance!

KAUST, in collaboration with MCIT, to host the second edition of the MENA Machine Learning Winter School (MenaML)

1 min read ·
A week of advanced instruction and exchange for emerging machine learning talent.

Efficient Machine Learning for Scientific and Medical Applications

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B4/5 L0 A0215

This dissertation addresses key challenges of machine learning in scientific and medical domains by developing methods that improve model efficiency, data efficiency, and learning under real-world constraints.

Uncertainty Quantification with Conformal Prediction in Energy Data

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B9 L2 R2325

The talk will introduce the fundamentals of conformal prediction (CP) - a flexible, model-agnostic uncertainty quantification framework for generating statistically valid uncertainty estimates in energy applications - and demonstrate how it can be layered on top of machine learning models to produce reliable prediction intervals.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations
Visiting student at King Abdullah University of Science and Technology (KAUST).

Racha Zayni

Visiting Researcher, Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Thoughts About Machine Learning

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B9, Lecture Hall 1, R-2322

A weekly seminar series from January 26 to April 20, 2026, exploring advanced AI concepts beyond the scope of standard deep learning courses.

Causal Representation Learning

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B1 L2 2202

In this talk, we will explore the latest advancements in the emerging field of causal representation learning (CRL).

From Images to Neural Scenes: Efficient Rendering, Robust Editing, and Human-Aware Reconstruction

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This thesis addresses these challenges by introducing efficient mesh-based distillation for real-time rendering, view-consistent text-guided scene editing, and human-aware reconstruction that improves geometry and camera estimation.

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Overcoming Catastrophic Forgetting: From Efficiency to Safety

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B3 L5 R5220

This thesis addresses the challenge of catastrophic forgetting in AI by developing novel continual learning methods that enhance memory efficiency in video analysis and preserve safety alignment in large language models, ensuring reliable adaptation in both resource-constrained and safety-critical applications.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent
KAUST-CEMSE-CS-VISION-CAIR-Dalal-Aldossary

Dalal E. Aldossary

M.S. Student, Computer Science
Dalal Aldossary is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

KAUST advances scalable AI through global collaboration

1 min read ·
KAUST brings global experts together to advance scalable AI and tackle the challenges of training large models.

Stochastic Interacting Particle Methods and Generative Learning for Multiscale PDEs

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B1 L3 R3119

This talk discusses stochastic interacting particle (SIP) methods for advection-diffusion-reaction PDEs based on probabilistic representations of solutions, and shows their self-adaptivity and efficiency in several space dimensions.

Innovative research earns Islam Ashry election as Fellow of the Institute of Physics

3 min read ·
Dr. Islam Ashry has been elected Fellow of the Institute of Physics in recognition of his impactful photonics-based research.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

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B4/5 L0 A0215

This thesis introduces a novel framework for asynchronous first-order stochastic optimization centered on heterogeneous worker speeds that remain fast, stable, and even provably optimal.

Zakaria El Kassimi

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Jonathan Klein

Research Scientist, Computer Science
My fields of research interests include computational simulation, plant simulation and modeling, synthetic training data, machine learning architectures, none-line-of-sight imaging, and computational photography.

AI and Mathematics: Efficient Machine Learning Algorithms Inspired by Dynamical Systems, Complex Analysis, and Embedding Theory

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B9 L3 R3120

This talk describes a novel machine learning algorithm, inspired by complex analysis and dynamical systems theory, that significantly improves efficiency in solving partial differential equations and scientific computing, while providing a theoretical framework for reconstructing complex, unknown systems from partial observational data.

KAUST Workshop on Distributed Training in the Era of Large Models

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Auditorium between B4 & 5, L0, R0215

Join leading researchers and innovators to explore how distributed training is reshaping the next generation of large-scale AI models.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

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B9 L2 R2325

This talk will discuss how to design asynchronous optimization methods that remain fast, stable, and even provably optimal.

Training Neural Networks at Any Scale

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B9 L2 R2325

The talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning, spanning domains from vision to language models.

AI for Science: AI-Driven Knowledge Discovery

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B1 L3 R3119

This talk focuses on the latest progress in this area, often called AI for Science, and specifically on how AI is revolutionizing scientific exploration and the uncovering of fundamental physical laws.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Yibin Wang

Keep Moving forward

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Hardware Centric Quantized Convolutional Neural Network and Algorithms

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B3 L5 R5209

This thesis addresses the challenges of deploying quantized convolutional neural networks (QCNNs) on resource-constrained edge devices by proposing two novel hardware-software co-design frameworks: one for deriving lightweight, hardware-friendly models validated on FPGA, and another for hardware-aware mixed-precision quantization on compute-in-memory accelerators.

Randomized Greedy Algorithms for Neural Network Optimization in Solving Partial Differential Equations

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B4 L5 R5220

This thesis introduces the randomized orthogonal greedy algorithm (ROGA) to bridge the gap between theoretical and practical performance of shallow neural networks for solving partial differential equations by overcoming key optimization challenges to achieve provably optimal convergence rates.

Andrea Vanessa Rocha

Research Specialist, Statistics

A blindfold approach improves machine learning privacy

1 min read ·
A query-based method for extracting knowledge from sensitive datasets without showing any underlying private data could resolve long-standing privacy concerns in machine learning.

Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation

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B9 L2 R2325

This seminar introduces GPU-accelerated Vecchia approximations to overcome Gaussian Process computational limits, enabling scalable applications for large geospatial datasets and high-dimensional computer model emulations.

Relations between Decision Trees and Decision Rule Systems

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B5 L5 R5220

This thesis investigates the complex transformation of decision rule systems into decision trees, establishing bounds on tree depth, developing polynomial-time path-finding algorithms and an optimal dynamic programming algorithm for minimum depth, and analyzing time-space complexity trade-offs for trees over infinite binary information systems.

On Complexity of Decision Trees for Decision Tables from Closed Classes

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B5 L5 R5220

This thesis considers classes of decision tables closed under operations of removal of attributes (columns) and changing of decisions and study for the tables from these classes relationships among the minimum complexity of deterministic and nondeterministic decision trees and the complexity of the set of attributes attached to columns of the table, while also analyzing the algorithms for constructing decision trees for these decision tables.
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Harnessing Multi-modal AI and Machine Learning for Next-Generation 6G Networks

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B9 L2 R2325

This seminar explores how multi-modal AI and large language models can optimize future 6G wireless networks by integrating diverse data sources to enhance reliability, efficiency, and overall performance.

Towards Trustworthy News Recommendation Systems

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B3 L5 R5209

This thesis enhances the trustworthiness of news recommendation systems by addressing the cold start problem for new users/items, enabling efficient user data removal for privacy, and mitigating multi-dimensional filter bubbles to improve diversity.

Making LLMs Agentic: How Tool Use and Synthetic Data Drive Real-World Intelligence

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B2/3 L0 R0215

This talk presents advancements in agentic AI, focusing on creating and utilizing datasets and models that enable Large Language Models to effectively interact with external tools for real-world applications.

SAAI Factory 3

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Buildings 20 and Buildings 19

SAAI Factory 3 brings together artists, researchers and youth to explore AI and art at KAUST.

Some Contributions to Particle and Unbiased Simulation Methods

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This thesis develops unbiased estimators and efficient particle filters, leveraging Feynman-Kac formulae and multilevel techniques, to address challenges in parameter estimation, filtering, invariant measure approximation, and stationary distribution computation for partially observed diffusion processes and McKean-Vlasov stochastic differential equations.

On the Use of "Conventional" Unconstrained Minimization Solvers for Training Regression Problems in Scientific Machine Learning

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B9 L2 R2325

This talk introduces PETScML, a framework leveraging traditional second-order optimization solvers for use within scientific machine learning, demonstrating improved generalization capabilities over gradient-based methods routinely adopted in deep learning.
Stochastic Numerics and Statistical Learning Workshop

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025

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Auditorium 0215 between B2 & B3

Explore the latest in stochastic algorithms, statistical learning and optimization at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2025.

Spatial Dataflow is all you need!

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B9 L2 R2325

Jian Weng's research, inspired by spatial dataflow, proposes a unified hardware design and implementation framework that leverages a familiar software programming paradigm, enabling concise and efficient designs while significantly reducing code complexity and achieving near-handcrafted performance, with future applications in ML inference systems targeting memory bandwidth limitations.
Decision Trees Versus Systems of Decision Rules

New book by KAUST Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov and Professor Mikhail Moshkov published by Springer

1 min read ·
New book by Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov, and Professor Mikhail Moshkov explores decision trees and rule systems using rough set theory.
KAUST-CEMSE-Public-Colloquium-Marco-Canini-Why-ML-Works-Its-Not-Magic

Why ML Works: It’s Not Magic, It’s Networked Systems Wizardry!

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Today’s machine learning (ML) solutions are achieving extraordinary success across diverse fields, thanks to their ability to learn complex models and deliver impressively accurate results. While the availability of big data is often credited as a driving force, the unsung hero behind ML’s rise is the rapid innovation in computing and software systems that make these breakthroughs possible and accessible.

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

Ph.D. Student, Computer Science
Ahmad Sait is a PhD student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Master's from KAUST and Bachelor's from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

MS Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

Circuits and Systems for Efficient Machine Learning and Artificial Intelligence

Machine Learning (ML) and Artificial Intelligence (AI) applications require the use of more and more advanced algorithms for extracting meaningful information from increasingly (and sometimes incredibly) large sets of data. While the algorithmic part has recently seen significant advances (as for instance through the adoption of Deep or Convolutional Neural Networks), it sometimes comes at the cost of high computational complexity which hinders their straightforward implementability. This activity aims at advancing in this direction by: proposing architectures to reduce the computational cost

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

Advanced Breach and Attack Simulation using ML

Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these

Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

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H2 B9

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Computational Methods for ChIP-seq Data Analysis and Applications

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B3 L5 5209

Abstract The development of Chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology has enabled the construction of genome-wide maps of protein-DNA interaction. Such maps provide information about transcriptional regulation at the epigenetic level (histone modifications and histone variants) and at the level of transcription factor (TF) activity. This dissertation presents novel computational methods for ChIP-seq data analysis and applications. The work of this dissertation addresses four main challenges. First, I address the problem of detecting histone modifications from

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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B3 L5 R5209

Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

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B3 L5 R5209

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

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B3 R5208

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural Inductive Matrix Completion (NIMC) in disease-gene prediction.

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

KAUST Research Workshop on Optimization and Big Data

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B19 L3 H2

The age of "big data" is here: data of unprecedented sizes is becoming ubiquitous, which brings new challenges and new opportunities. With this comes the need to solve optimization problems of unprecedented sizes.

Bio2Vec Kickoff Meeting

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Paris France

​​Research Scientist Senay Kafkas will be attending the Bio2Vec kickoff meeting in Paris, France.

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

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B2 L5 R5220

Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a

AI4GH Seminar Series - Vector Representation of Biological Entities Based on Ontologies and Their Annotations

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B2 R5220

Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a biological entity with a set of phenomena within the domain.

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Prediction of active and inactive chemical compounds from high-throughput assays

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B5 L5 R5209

Abstract This study considers chemical compounds that can exert their activity by interacting with a target protein or other molecular receptor. Our aim is to develop machine learning models that can predict if a chemical compound will be active in a particular test/assay. We will use data from assays that are present in the PubChem knowledge base, specifically in its segment called BioAssays which reports the results of many high-throughput screening experiments. PubChem BioAssays is a valuable resource that contains information from a large number of experiments. In one assay, sometimes many

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

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B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Semantic Prioritization of Novel Causative Genomic Variants in Mendelian and Oligogenic Diseases

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B9 R4223

Abstract Recent advances in Next Generation Sequencing (NGS) technologies have facilitated the generation of massive amounts of genomic data which in turn is bringing the promise that personalized medicine will soon become widely available. As a result, there is an increasing pressure to develop computational tools to analyze and interpret genomic data. In this dissertation, we present a systematic approach for interrogating patients' genomes to identify candidate causal genomic variants of Mendelian and oligogenic diseases. To achieve that, we leverage the use of biomedical data available

Biomedical Signal Processing using the Squared Eigenfunctions of the Schrödinger Operator and Machine Learning

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B1 B4 B4214

Abstract The health of a human body is monitored through several physiological measurements such as the heart rate, the blood pressure, the oxygen saturation levels, brain activity, etc. These measurements are taken at predefined points in the body and recorded as temporal signals or colorful images. During the diagnosis phase, physicians analyze these records visually (sometimes it is not visual with the progress in medicine, better to say: sometimes visually) to take treatment decisions. These records are usually contaminated with noise. The origin of this noise may be diverse. For instance

ML Hub Seminar Series | The Machine Learning (ML) Hub

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B9 H2 R2325

The Machine Learning Hub @ KAUST is designed to be the one-stop-shop for machine learning (ML) and artificial intelligence (AI) at KAUST. It is an informal forum for exchanging ideas in these areas, including (but not limited to) theoretical foundations, systems, tools, and applications. It will be providing several offerings to the KAUST community interested in ML and AI, including a regular seminar series where new research in the field is presented, an online social forum dedicated to AI and ML discussions, announcements, brainstorming, collaborations, and hands-on activities (e.g

The Evolution of Machine Learning

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B4 B5 A0215

This talk presents an overview of recent methodological advances in developing item-based nearest-neighbor-based recommender systems that have substantially improved their performance. Biography George Karypis is a Distinguished McKnight University Professor and an ADC Chair of Digital Technology at the Department of Computer Science & Engineering at the University of Minnesota, Twin Cities. His research interests span the areas of data mining, high performance computing, information retrieval, collaborative filtering, bioinformatics, cheminformatics, and scientific computing. His research has

Deep Predictive Models for Mining Electronic Health Records

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B1 L4 R4214

There is an increasing growth in the amount of electronic health records (EHRs) being collected by healthcare facilities. Data mining techniques hold great potential to systematically use such data for identifying not only inefficiencies but also best practices that improve care and reduce costs. However, due to the complexity of EHR data, directly applying traditional machine learning techniques may yield unsatisfactory predictive performance. Recent advances in deep learning-based methods provide unprecedented ability to predict patients’ future health status, but they still suffer from the sparsity issue of EHR data.

Convolutional Neural Networks on Graphs

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B9 H2

In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for grid-structured data, while many important applications have to deal with graph-structured data.

Day 2 - Deep learning and the latest algorithms in artificial intelligence

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B4 B5 A0215

The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises. Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python. Note, that this course has limited seating and filling registration form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.

From Bioinformatics to Translational and Health Informatics: Whitening the Black Box

Prof. Yasser EL-Manzalawy, Information Sciences and Technology, Pennsylvania State University

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B9 H1

Machine learning has been extensively used in developing predictive models for a variety of bioinformatics tools. In many of these applications, to achieve the highest possible predictive performance, black box models (e.g., SVMs and Neural Networks) had been preferred over white box models (e.g., Decision Trees and Rule-based models). First, I argue that sacrificing interpretability for the sake of performance is a reasonable decision for many bioinformatics applications. However, I will demonstrate that uncareful utilization of black box models can lead to misinterpretations of the results.

Day 1 - Deep learning and the latest algorithms in artificial intelligence

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B4 B5 A0215

The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises. Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python. Note, that this course has limited seating and filling registration form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.

Sim-to-Real Transfer for Autonomous Navigation

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B2 L5 R5220

This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator Sim4CV which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning.

Random Matrix Theory: Selected Applications from Statistical Signal Processing and Machine Learning

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B1 L4 R4214

Random matrix theory is an outstanding mathematical tool that has demonstrated its usefulness in many areas ranging from wireless communication to finance and economics. The main motivation behind its use comes from the fundamental role that random matrices play in modeling unknown and unpredictable physical quantities. In many situations, meaningful metrics expressed as scalar functionals of these random matrices arise naturally. Along this line, the present work consists in leveraging tools from random matrix theory in an attempt to answer fundamental questions related to applications from statistical signal processing and machine learning.

Traffic Monitoring and MAC-Layer Design for Future IoT Systems

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B1 L4 R4214

The advances in the technology and the emergence of low complexity intelligent devices result in the evolution of the Internet-of-Things (IoT). One of the most important resources is the wireless channel, which is a shared resource; thus, it is necessary for the nodes to have methods that schedule channel access. This thesis considers the problem of distributed sensing and channel access in the context of IoT systems, where a set of selfish nodes competes for transmission opportunities. In the channel access part, a memory-one channel access game is proposed to reduce the collision rate, to enhance the cooperation among the nodes, and to maximize their payoffs by optimizing their channel access probabilities, based on the channel state in the previous time step.

Wireless Network Intelligence @ the Edge

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B1 L3 R3119

In just a few years, breakthroughs in machine learning (ML) and particularly deep learning have transformed every aspects of our lives from face recognition, medical diagnosis, and natural language processing. This progress has been fueled mainly by the availability of more data and more computing power. However, the current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing, sifting through this data for inference.

Modern Techniques of Statistical Optimization for Machine Learning

Tong Zhang, Professor of Computer Science and Mathematics, HKUST

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B9 H1

Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through selected machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, and generative models.

Efficient Localization of Human Actions and Moments in Videos

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B3 L5 R5220

Abstract We are stumbling across a video tsunami flooding our communication channels. The ubiquity of digital cameras and social networks has increased the amount of visual media content generated and shared by people, in particular videos. Cisco reports that 82% of the internet traffic would be in the form of videos by 2022. The computer vision community has embraced this challenge by offering the first building blocks to translate the visual data in segmented video clips into semantic tags. However, users usually require to go beyond tagging at the video level. For example, someone may want

Zero in on computational challenges in Nanopore sequencing

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B9 L2 H1

In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. Then, I will further introduce their applications in clinical and environmental fields.

WOMoCoE 2019 | Keynote Presentation-Evaluating ontology modules from the perspective of machine learning @JOWO 2019

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Graz Austria

KAUST Assistant professor Robert Hoehndorf will be giving a keynote presentation at the 4th International Workshop on Ontology Modularity, and Evolution @ JOWO 2019, Graz, Austria.

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Machine Learning Models for Context Aware Recommendation Systems

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B3 L5 R5209

The goal of this thesis is to pave the way towards the next generation of recommendation systems tackling such real-world challenges to improve the user experience while giving good recommendations.

Langevin Monte Carlo as an optimization algorithm

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B9 L2 H1 R2322

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Practical Cryptographic Framework to Manage Security and Privacy for IoT Endpoint devices

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B1 L4 R4214

In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.

Seeing is Believing? Media Integrity in a Post-Truth World

Prof. Nasir Memon, Vice Dean for Academics and Student Affairs and Professor of Computer Science and Engineering at the New York University Tandon School of Engineering

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B9 L2 H1

The emergence of “fake news” along with sophisticated techniques using machine learning to create realistic looking media such as deepfakes, has led to a renewed interest in digital media forensics. In this talk, Professor Nasir Memon will broadly discuss how media is generated and manipulations have been traditionally detected. He will then look at new approaches using machine learning for creating media that are leading us to a world where images and video cannot be believed any more as they can evade traditional detection techniques. Professor Memon will end by discussing approaches that are being developed to return integrity and trust in digital media.

Amazon Web Services (AWS) ML Immersion Day

Mohib Khan, Hesham Abouelmagd, Shijaz Abdulla (AWS)

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B19 H1

The ML Hub with the support of the AI Initiative, is excited to be hosting the AWS ML Immersion Day! Join us for a full-day immersion tutorial and hands-on lab on Amazon’s ML tools. The program includes an introduction to AWS AI and machine learning services and hands-on module on Amazon Lex and SageMaker.

Dynamics and Convergence of Weight Normalization for Training Neural Networks

Guido Montufar, Assistant Professor, Departments of Mathematics and Statistics, University of California, Los Angeles (UCLA)

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B1 L3 R3119

We present a result on the convergence of weight normalized training of artificial neural networks. In the analysis, we consider over-parameterized 2-layer networks with rectified linear units (ReLUs) initialized at random and trained with batch gradient descent and a fixed step size. The proof builds on recent theoretical works that bound the trajectory of parameters from their initialization and monitor the network predictions via the evolution of a ''neural tangent kernel'' (Jacot et al. 2018). We discover that training with weight normalization decomposes such a kernel via the so called ''length-direction decoupling''. This in turn leads to two convergence regimes. From the modified convergence we make a few curious observations including a natural form of ''lazy training'' where the direction of each weight vector remains stationary.

Machine Learning Scan of Complete Genome Sequences of 4,500 Rice Varieties reveals Secrets in the Domestication Process of Crops

Takashi Gojobori, Distinguished Professor, Bioscience

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B9 L2 H2 R2325

In the history of humankind, domestication was invented as anthropogenic evolution that fulfills mankind’s critical food demand. The domestication is simply based on mating processes and subsequent selection processes to pick up better hybrid offspring that have advantageous combinations of genomes. Taking advantage of machine learning classifier, we discovered a number of sub-genomic regions that have been incorporated in the rice genomes through production of hybrid offspring during domestication. This so-called “introgression” event is disclosed as an essential key of domestication process. This eventually leads to construction of the AI-aided Smart Breeding Platform to accumulate all the breeding histories of crop species into an Integrated Breeding Knowledgebase.

Novel computational methods for promoter identification and analysis

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B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.

Understanding a Block of Layers in Deep Neural Networks: Optimization, Probabilistic and Tropical Geometric Perspectives

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KAUST

In this dissertation, we aim at theoretically studying and analyzing deep learning models. Since deep models substantially vary in their shapes and sizes, in this dissertation, we restrict our work to a single fundamental block of layers that is common in almost all architectures. The block of layers of interest is the composition of an affine layer followed by a nonlinear activation function and then lastly followed by another affine layer. We study this block of layers from three different perspectives. (i) An Optimization Perspective. We try addressing the following question: Is it possible that the output of the forward pass through the block of layers highlighted above is an optimal solution to a certain convex optimization problem? As a result, we show an equivalency between the forward pass through this block of layers and a single iteration of certain types of deterministic and stochastic algorithms solving a particular class of tensor formulated convex optimization problems.

Visible Light Communications Technology and Machine Learning - 2020-10-20

Paul Anthony Haigh, Newcastle University, United Kingdom and Dr. Bo Tan, Tampere University, Finland

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KAUST

In this webinar we will give an overview of several key technologies related to our special issues in Frontiers of Communications and Networks; (1) visible light communications (VLC) and (2) applications of machine learning in optical and wireless communication systems.

Challenges in Marine Autonomy

Stefan B. Williams, Head of School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney

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KAUST

Featured Sessions
For more information please visit the website

AI for Cybersecurity vs. Cybersecure AI: a chicken and egg problem?

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KAUST

Abstract Artificial Intelligence (AI) is a top subject in the expectations about emerging problem-solving technologies in a number of sectors, given advances in their practicality. Cybersecurity is one such target, and like any hype subject appearing in the homonym Gartner curve, there is a chance that expectations are somewhat inflated. In this talk, I will start by briefly reviewing how AI/ML have been showing promise in a number of security and dependability areas. Then I will play devil’s advocate, challenging the audience to think whether AI/ML mechanisms can help cybersecurity, without

Enabling Safe Cyber-Physical System autonomy with AI

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KAUST

Abstract With the advent of increasingly intelligent algorithms, robots and other cyber-physical systems are capable of planning and performing increasingly challenging and creative tasks. Safety, however, remains an essential requirement on many robotic behaviors. It is also a property that is hard or impossible to prove for virtually all intelligent algorithms of practical value. Ariadne is a constructive, model-based paradigm that enables the safe operation of many robotic systems, even though the algorithms involved with the operation may not be verifiable. Ariadne, or "plan B" engineering

Practical Lessons and Opportunities from Testing Autonomous Mobility at KAUST

Mohamed Abdel-Aal

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KAUST

Abstract KAUST is a living lab for future mobility and transportation innovations such as autonomous driving. The roads, the progressive community, and the traffic regulations make KAUST a great place to examine and assess various mobility options in collaboration with industry partners, government agencies, and startups. KAUST launched the first autonomous shuttles pilot in Saudi Arabia in December 2019. The pilot is a platform to develop unique smart city experiences that enable the scientific community, the residents of KAUST, our partners, and entrepreneurs to create an innovative

Fish Behavior Recognition and Data-driven Control for Smart Aquaculture

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KAUST

Abstract In this talk, I will present our progress in controlling fish feeding and monitoring water quality in smart aquaculture systems. The idea consists of combining computer vision tools along with optimal control strategies. I will first show some recent results on implementing reinforcement learning for optimal fish feeding and temperature monitoring. Then, I will discuss the implementation of computer vision tools as an essential component for analyzing fish behavior. I will conclude with some algorithms for the synthesis of computer vision-based classifiers that enable control

KAUST Smart Health Initiative: Importance of AI and ML

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KAUST

Abstract Data collection, storage and management has been always the cornerstone of any successful and efficient healthcare system. The recent rapid and impressive advances in data collection tools such as artificial intelligence (AI) technology, and its deep-learning subtype in particular, provide a unique opportunity of using data in a more efficient and cost-effective ways. Indeed, AI and ML (Machine Learning)-based management of health data is revolutionizing our approaches to disease treatment and data handling at every level that led to, among other things, the birth of personalized and

Physics-based Modeling Meets Machine Learning

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KAUST

Abstract The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in scientific and visual computing. This talk covers a selection of previous and current work presenting various synergy effects between physics-based modeling and machine learning ranging from the generation and the enhancement of synthetic data to data-driven approaches boosting the performance of physics simulations. Brief Biography Since summer 2016, Prof. Michels is heading the Computational Sciences Group

Counterfactual Representation Learning with Balancing Weights

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KAUST

Abstract A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. In this work, we discuss the pitfalls of these strategies - such as a steep trade-off between achieving balance and predictive power - and present a remedy via the integration of balancing weights in causal learning. Specifically, we theoretically link balance to the quality of propensity estimation, emphasize the importance of identifying a proper target population, and elaborate

Recent Advances in Optimization for Machine Learning

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KAUST

Abstract In this talk I will briefly and at a high level outline several recent (obtained in the last 2 years) research results obtained in the Optimization and Machine Learning lab at KAUST. In general, we work on the development of fundamental optimization algorithms for machine learning, including randomized, parallel, distributed, decentralized and federated algorithms, and the associated mathematical theory. Our goal is typically the development of new methods that would have state-of-the-art theoretical properties, with the explicit aim for these methods to be also practical. I may touch

From Private Learning to Trustworthy Learning

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KAUST

Abstract Recent research showed that most of the existing machine learning algorithms are vulnerable to various privacy attacks. An effective way for defending these attacks is to enforce differential privacy during the learning process. As a rigorous scheme for privacy preserving, Differential Privacy has now become a standard for private data analysis. In this talk, I will present some recent developments in trustworthy machine learning from a differential privacy view. In the first part, I will talk about some pitfalls and trustworthy issues (such as resilience and fairness) in the current

Training Machines to Recognize Reliable Protein-Protein Docking Pose

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KAUST

Abstract Protein-protein interactions drive many important biological events such as infection, replication, and recognition. To control or engineering such events, we need to access the molecular details of the interaction provided by experimental 3D structures. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions. Computational modeling like protein-protein docking can help to fill this gap by generating docking poses. Protein-protein docking generally consists of two parts, sampling, and

Recent Topics in Generative Modeling

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KAUST

Abstract In this talk, I will talk about recent topics in generative modeling. I will discuss some interesting research directions in the research community in general and also mention some works of my own group. Brief Biography Peter Wonka is Full Professor in Computer Science at King Abdullah University of Science and Technology (KAUST) and Associate Director of the Visual Computing Center (VCC). Peter Wonka received his doctorate from the Technical University of Vienna in computer science. Additionally, he received a Masters of Science in Urban Planning from the same institution. After his

Accelerated Deep Learning via Efficient, Compressed and Managed Communication

-

KAUST

Abstract Scaling deep learning to a large cluster of workers is challenging due to high communication overheads that data-parallelism entails. This talk describes our efforts to rein in distributed deep learning's communication bottlenecks. We describe SwitchML, the state-of-the-art in-network aggregation system for collective communication using programmable network switches. We introduce OmniReduce, an efficient streaming aggregation system that exploits sparsity to maximize effective bandwidth use. We touch on our work to develop compressed gradient communication algorithms that perform

Attackability of Machine Learning Models with Applications in the Discovery of Safety-critical Aerial Operations

Prof. Xiangliang Zhang

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KAUST

Abstract In safety-critical application domains, it is crucial to assess the attackability of the employed machine learning models and to design fault-tolerant models against the noise and attacks. This talk will first introduce our study on characterizing the attackability of a targeted classifier on categorical sequences and a targeted multi-label classifier under evasion attack. A fault-tolerant model is then presented to handle the noisy data existing in the machine learning input. Our application aim is to find safety-critical aerial operations. We will introduce the development of a deep

AI and Health – Challenges and Opportunities

-

KAUST

Abstract There is a fair amount of hope, if not hype, to the extent that computational techniques, including AI, can transform our understanding of biology, early discovery of diseases, and augment health. From some real-world examples from our work, I'll discuss challenges and opportunities in this space. Brief Biography Professor at KAUST (08/2016), affiliated with Bioscience, Computer Science, Statistics, and Bioengineering programs. He had been acknowledged with honors for best teaching; physiology & neuroscience to medical students in Medical School (Karolinska Institute) and machine

AI-enabled Analysis of Our Social Expression and Interactions in Digital Age

Prof. Xiangliang Zhang

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KAUST

Abstract Half of the total global population are using social media and produce a huge amount of user-generated content and interaction records on Facebook, Twitter, Instagram, YouTube, LinkedIn and so on. AI and machine learning techniques have been leveraged to understand the unprompted feelings, opinions and preference of users by analyzing their social expression and interactions. This talk will introduce the advantages brought by the AI-powered recommendation systems, from which we have benefited to have an easy discovery of the posts we like to see on our favorite social networks, the

Revealing functional and phenotypic consequences of molecular aberrations through ontologies

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KAUST

Abstract An ontology is an explicit specification of a conceptualization of a domain. Biomedical ontologies are a foundation of bioinformatics infrastructure and are used both to integrate data and to formally describe biological phenomena. I will show how to use ontologies to reveal consequences of molecular changes and aberration. Brief Biography Robert Hoehndorf is an Associate Professor in Computer Science at KAUST. His research focuses on the development and application of knowledge-based methods in biology and biomedicine.

Automation and Insights for Improved Oilfield Operations

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KAUST

Abstract The challenges experienced by the oil and gas sector over the past decade have been a driver for change and technology adoption. In addition to an overview of oilfield applications that continue to experience the benefits of today’s digital transformation, the talk will shed some light on oilfield research and technology developments at KAUST. Brief Biography Shehab Ahmed is Professor of Electrical & Computer Engineering. He holds a joint appointment as Professor of Energy Resources and Petroleum Engineering at KAUST and is a member of the Ali I. Al-Naimi Petroleum Engineering

The AI Initiative at KAUST

-

KAUST

Brief Biography Bernard Ghanem is currently an Associate Professor in the CEMSE division, a theme leader at the Visual Computing Center (VCC), and the Interim Lead of the AI Initiative at KAUST. His research interests lie in computer vision and machine learning with emphasis on topics in video understanding, 3D recognition, and theoretical foundations of deep learning. He received his Bachelor’s degree from the American University of Beirut (AUB) in 2005 and his MS/PhD from the University of Illinois at Urbana-Champaign (UIUC) in 2010. His work has received several awards and honors, including

Towards AI-based precision medicine in breast cancer – from molecular-, to imaging-, to radiogenomics analysis

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KAUST

Abstract Breast cancer is the leading cause of cancer-related death in women worldwide. A promising strategy to improve breast cancer survival rates is precision medicine--a targeted approach to the diagnosis and treatment that are tailored for individual patients considering their genetic, environmental, and lifestyle backgrounds. In this talk I will introduce our series of work towards precision medicine in breast cancer. I will first describe, at a pan-cancer level, how we used as few as 58 genomic features to build accurate predictors to detect cancer patients from normal people, to

Imagination supervised Machines that can See, Create, Drive, and Feel

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KAUST

Abstract Most existing learning algorithms can be categorized into supervised, semi-supervised, and unsupervised methods. Most of these approaches rely on defining empirical risks or losses on the provided labeled and/or unlabeled data. In this talk, we will reflect on a class of methods that goes beyond labeled/unlabeled training data, improving task performance by either raw or latent imaginary data. We refer to this class of techniques as imagination supervised learning, and we will dive into how we developed several approaches to build machine learning methods that can See, Create, Drive

Intelligent Design and Discovery of Molecules

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KAUST

Abstract Quantitative structure-property relationships (QSPRs) using machine learning tools to relate molecular structural features of pure hydrocarbons to a range of physical and chemical properties is of interest. Mathematical modelling of these relationships can be very complex, which makes the deep learning networks (DNNs) ideal candidates for the QSPR development. Here we show QSPR development for a chemical property related to the sooting tendency of a fuel, which is directly linked to high and undesirable particulate matter emissions to the atmosphere, and therefore is recognized as a

Next-Gen Mapping and Monitoring of Coral Reefs with SLAM and SfM

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KAUST

Abstract The Red Sea Development Company (TRSDC), one of Saudi’s premiere GigaProjects, aims to develop a world-class, hyper-luxury resort complex in the Red Sea. The project is located between the coastal cities of Umm Lujj and Al- Wajh, encompassing one of the Red Sea’s most unique geological and biological habitats, the Al-Wajh lagoon. This location includes the Red Sea’s largest barrier reef and the Red Sea’s largest lagoonal habitat (1500km2), within which can be found nearly 100 islands and many species not known from any other part of the world. As a result of TRSDC’s commitment to

Artificial Intelligence(AI) Inspired Designs of Acoustic and Photonics Systems

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KAUST

Abstract In this talk, I will introduce our recent progress on how deep learning models inspired us in solving real world problems with different applications. In particular, I will focus on deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak, which can conceal an object from incident acoustic waves over a broad frequency range; and a deep learning framework based on fully connected neural network to design plasmonic metascreen for efficient light trapping in ultrathin silicon solar cells. Brief Biography Dr. Ying Wu is an associate professor in

On Solving a Key Challenge in Federated Learning: Local Steps, Compression and Personalization

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KAUST

Abstract We propose a new optimization formulation for training federated learning models. The standard formulation has the form of an empirical risk minimization problem constructed to find a single global model trained from the private data stored across all participating devices. In contrast, our formulation seeks an explicit trade-off between this traditional global model and the local models, which can be learned by each device from its own private data without any communication. Further, we develop several efficient variants of SGD (with and without partial participation and with and

RNA secondary structure prediction – revisiting an old problem from new perspectives

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KAUST

Abstract RNAs need to form specific secondary structures for their functions. RNA secondary structure prediction is among the early problems being tackled in bioinformatics, yet far from being solved. The dominant dynamic programming-based methods cannot handle pseudoknots, which are present in about 40% of RNAs, whereas heuristic methods and recent deep learning methods still do not have satisfactory performance. In this talk, I will introduce our two recent works, NNfold and E2Efold, both of which are deep learning-based methods for RNA secondary structure prediction. NNfold predicts the

Machine Learning for Science: Learning Representations and Governing Equations from Scientific Data

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KAUST

Abstract In essence, science is about discovering regularities in Nature. It turns out that such regularities (laws) are written in the language of mathematics. For example, fundamental equations such as Maxwells, Newtons, mean-field, and renormalization techniques in statistical mechanics enable deep understanding, often in combination with computational modeling and simulations. In many cases, laws can be formulated and refined from fundamental "first principles" such as the variational principle of least action in mechanics. Yet, in phenomenological areas, including biology, we have data

IMCP: An Efficient In-Memory Convolutional Neural Network (CNN) Processor

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KAUST

Abstract Even though CNNs achieve great success in performing complex intelligence tasks, this achievement comes with an overwhelming cost. Modern CNN architectures require hundreds of stacked convolution layers, performing billions of operations for a single input. To meet the unique needs of AI-based systems, various architectures were proposed as possible contenders such as GP-GPUs or application-specific processors like Google TPU and Intel Movidius. These architectures focus on utilizing massive parallelism to address the computational needs; however, the issue of memory access is still

Stylistic and spatial disentanglement in GANs

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KAUST

This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.

On Recently Developed Non-Gaussian Priors and Sampling Methods with Application to Industrial Tomography

Prof. Lassi Roininen, Applied Mathematics, LUT University

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B1 L4 R4214

We consider two sets of new priors for Bayesian inversion and machine learning: The first one is based on mixture of experts models with Gaussian processes. The target is to estimate the number of experts and their parameters, and to make state estimation. For sampling, we use SMC^2. For non-Gaussian priors, we discuss Cauchy priors and the generalisation to high-order Cauchy fields and further generalisation to alpha-stable fields. For sampling, we use a selection of modern MCMC tools. Finally, we apply some of the methods and models to an industrial tomography problem on estimating log internal structure, measured at sawmills, based on X-ray, RGB camera and laser scanning.

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

KAUST Introduction on Machine Learning Bootcamp

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B16 L3

KAUST Introduction on Machine Learning Bootcamp As part of KAUST’s mission to disseminate skills and teach hands-on experience in machine learning on-campus, the first KAUST Introduction to Machine Learning Bootcamp will be held for three full days on March 29, 30, and 31. This bootcamp is an opportunity for students and researchers at KAUST to acquire basic skills necessary to apply Machine Learning and Deep Learning techniques to solve problems in their fields of expertise. It will span three consecutive days, with the first day focusing on basic theoretical concepts (lectures) and the

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

High-dimensional Regression and Dictionary Learning: Some Recent Advances for Tensor Data

Prof. Waheed Bajwa, Electrical and Computer Engineering, Rutgers University-New Brunswick

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B1 L3 R3119

Data in many modern signal processing, machine learning, and statistics problems tend to have tensor (aka, multidimensional / multiway array) structure. While traditional approaches to processing of such data involve 'flattening' of data samples into vectors, it has long been realized that explicit exploitation of tensor structure of data can lead to improved performance. Recent years, in particular, have witnessed a flurry of research activity centered around development of computational algorithms for improved processing of tensor data.

To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

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KAUST

Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.

CBRC Dual Seminar - Analysis of COVID-19 Severity - DeepSVP computational method

Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student

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KAUST

Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.

Communication-Efficient Distributed Newton-type Algorithms for Federated Learning

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B9 L2 R2322 H1

In this talk, I will discuss communication compression and aggregation mechanisms for curvature information in order to reduce these costs while preserving theoretically superior local convergence guarantees.

Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

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KAUST

In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.

Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

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B9 L2 R2322 H1

This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

Optimal Algorithms for Affinely Constrained, Distributed, Decentralized, Minimax, and High-Order Optimization Problems

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B5 R5220

In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.

A new class of random field models for data on networks

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B9 L2 R2322 H1

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

On the resolution of a theoretical question related to the nature of local training in federated learning

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B1 L3 R3119

In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.

Applied AI for sustainable development

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B9 L2 R2322 H1

Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.

Wide Bandgap Semiconductor Device Design via Machine Learning

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B2 L5 R5220

This dissertation presents novel approaches to the design of electrical and optical wide bandgap semiconductor devices, which opens new avenues for future research. It is possible that it might be used in a broad variety of sectors, including illumination, sensing, disinfection, and power devices by using TCAD and machine learning to deliver quick forecasts of the III-nitride semiconductor device.

Machine learning in hardware via trained metasurface encoders: theory, design and applications

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B2 L5 R5209

In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).

Parameter-Free Online Optimization: Past, Present, and Future

Francesco Orabona, Associate Professor of Electrical and Computer Engineering, Boston University

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B9 L2 R2322

Parameter-free online optimization is a class of algorithms that does not require tuning hyperparameters, yet they achieve the theoretical optimal performance. Moreover, they often achieve state-of-the-art performance too. An example would be gradient descent algorithms completely without learning rates. In this talk, I review my past and present contributions to this field. Building upon a fundamental idea connecting optimization, gambling, and information theory, I discuss selected applications of parameter-free algorithms to machine learning and statistics. Finally, we conclude with an overview of the future directions of this field.

Characterization of Natural Bacterial Pathogens with Genomic and Predictive Modelling

Dr. Danesh Moradigaravand, Infectious Disease Epidemiology lab, BESE, KAUST

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B3 L5 R5209

In this talk, I will first present how the application of phylogenetic and phylodynamic methods to whole genome sequencing data of multidrug resistant bacterial pathogens provided an in-depth understanding of the epidemiology and evolution of these strains on epidemiological time scales. I will then discuss the characterization of the genomic repertoire of bacterial traits using a combination of machine learning, whole genome sequencing and large-scale phenotypic assays. I will then present the leverage of predictive modelling to predict bacterial features, e.g. antimicrobial resistance, growth, and horizontal gene transfer, from genomic biomarkers. I will finally discuss how large-scale phenotypic assays enabled us to identity genes underlying morphogenesis and biofilm formation.

The Wild West of NLP Modeling, Evaluation, and Documentation

Dr. Nazneen Rajani, Research Lead, Hugging Face, California

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B2 L5 R5220

Are commercial APIs from Microsoft, Google, and Amazon for NLP tasks any better than rule-based systems? Is documentation for LLMs accessible to non-expert users in the industry? Our work on creating a unified toolkit for evaluation (robustness gym) and reporting (interactive model cards) attempts to address these questions.

Towards Richer Video Representation for Action Understanding

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B2 L5 R5209

With video data dominating the internet traffic, it is crucial to develop automated models that can analyze and understand what humans do in videos. Such models must solve tasks such as action classification, temporal activity localization, spatiotemporal action detection, and video captioning. This dissertation aims to identify the challenges hindering the progress in human action understanding and propose novel solutions to overcome these challenges.

Machine Learning for Biomedical Engineering: from Oncological Electroporation to Gesture Recognition

Luciano Tarricone, Department of Innovation Engineering, University of Salento

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B9 L2 H2

In this presentation an overview is given on the most important research activities performed at the EML2.

Machine-learning-assisted Quality of Transmission Estimation in Optical Networks

Andrea Bianco, Full Professor, Electronics and Telecommunications Department at Politecnico di Torino

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B9 L4 R4125

Machine Learning (ML) tools have recently been adopted for a wide range of automated operations in optical networking, moving fundamental steps towards the paradigm of zero-touch infrastructures. One example of such tasks is estimating the Quality of transmission of a lightpath prior to its establishment, which is particularly challenging due to the non-linear characteristics of signal propagation in optical fibers and to the often-incomplete knowledge of equipment parameters. This talk provides an overview of the contribution of my research team in the field of ML-based lightpath QoT estimation, including transfer learning approaches for inter-domain model adaptation, active learning for model building with small-sized training dataset, quantification of prediction uncertainty, and adoption of Explainable AI framework to expose the internal decisional mechanisms of trained models.

Machine Learning for Scalable MIMO Communication Systems: From Theory to Real-World Demonstration

Dr. Ahmed Alkhateeb, Electrical Engineering , Arizona State University

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B9 L2 H1

Large-scale MIMO communication (at sub-6GHz, mmWave, and sub-THz bands) is a key enabler for 5G, 6G, and beyond. Scaling MIMO systems, however, is subject to critical challenges, such as the large channel acquisition and beam training overhead and the sensitivity to channel estimation errors (especially at lower frequencies) and blockages (at higher frequencies). These challenges make it difficult for MIMO systems to support applications that have high mobility and strict reliability constraints. In this talk, I will first motivate the use of machine learning and sensory data to address these challenges. Then, I will present a few key machine-learning roles, enabling datasets, and recent hardware proof-of-concept prototypes that demonstrate the machine-learning gains in real-world environments.

Scaling up reef ecosystem monitoring using underwater robots

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B18 Fellowship Hall

Featured Sessions
Abstract The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to capture the full variation and complexity of interactions between different reef organisms and their habitat. The CUREE AUV platform provides a unique set of capabilities in the form of robot behaviors and perception algorithms to enable scientists to explore different aspects of an ecosystem. Examples of these capabilities include low-altitude visual surveys, soundscape

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

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B9

Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

Exploiting Data Sparsity in Matrix Algorithms for Adaptive Optics and Seismic Imaging

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B2 R5209;

This thesis addresses the exponential growth of experimental data and the resulting computational complexity seen in two major scientific applications, which account for most cycles consumed on today's supercomputers.

Physics-based Simulation in Visual Computing

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B9 L2 H2 H2

This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.

Universal light encoders: artificial intelligent optical hardware for real-time hyperspectral imaging and ultrasensitive detection

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B9 L2 H2

In this talk, I will review the fundamentals and application of this technology in various areas, ranging from the inverse design of ultra-flat optical components to HyplexTM, an innovative camera for acquiring and processing high-resolution hyperspectral videos in real-time at 30 frames per second.

Mitigating Bias in Predictions from Machine Learning Models

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B9 L2 H2 H2

The increasing popularity of machine learning models in real-world automated and decision support systems has underscored the need for assessing and then mitigating biases that may manifest, often spuriously, in their predictions either at the population, sub-population, or individual level. These biases can be assessed in terms of calibration, performance stratification, fairness metrics, prediction interval coverages, etc., and are mainly due to poor model specification (e.g., overparameterization without regularization or loss/likelihood mismatch) or data collection issues (e.g., population misrepresentation or unmeasured confounders).

Security and privacy in centralized and decentralized machine learning

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B4 L5 R5209

Machine learning (ML) is vulnerable to security and privacy attacks. Whereas security attacks aim at preventing model convergence or forcing convergence to wrong models, privacy attacks attempt to disclose the data used to train the model.

Adaptive Optimization Algorithms for Machine Learning

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B1 L4 R4214

Machine learning assumes a pivotal role in our data-driven world. The increasing scale of models and datasets necessitates quick and reliable algorithms for model training. This dissertation investigates adaptivity in machine learning optimizers.

FADO: A Federated Learning Attack and Defense Orchestrator

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B5 L5 R5209

Federated Learning (FL) is a distributed machine learning approach that allows multiple parties to train a model collaboratively without sharing sensitive data.

Efficient Machine Learning across Hardware, Software, and Algorithms

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B2 L5 R5209

Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.

Thoughts About Machine Learning - Week 7

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B9 LH1 R2322

The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence.

Thoughts About Machine Learning - Week 8

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B9 H1 R 2322

The AI Initiative, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division are delighted to announce the resumption of a series of seminars titled “Thoughts About Machine Learning". The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence. Attendance is

Models, Methods, Applications: HPC in Medicine, Science, Engineering, and AI

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B3 L5 R5209

Abstract The accurate numerical prediction of complex processes in medicine, science, and engineering requires a well balanced interplay of mathematical models, numerical methods, and scientific software. In this talk, we discuss along different examples from engineering, earth-sciences, and medicine the various challenges in building predictive models. Our focus will be on the algorithmic aspects, i.e. on the numerical methods, -may it be finite elements or neural networks in the context of massive parallelism. We will present in particular parallel preconditioners for non-linear problems

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

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B2 B3 A0215

We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Auditorium 0215 b/w B4&5, from May 19-30, 2024. Following the highly successful last two years edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2024 workshop aims to build on the achievements of last year's event, which featured 30 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022 and 2023, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, CUHK Shenzhen, and Imperial College London, among others.

Emaad Manzoor has defended his master thesis

1 min read ·
Emaad Manzoor has defended his master thesis on "Scheduling Broadcasts in a Network of Timelines" and will join Stony Brook University for Ph.D.

InfoCloud has published a paper "Progress and challenges in bioinformatics approaches for enhancer identification" in Briefings in Bioinformatics 2015

1 min read ·
InfoCloud has published a paper "Progress and challenges in bioinformatics approaches for enhancer identification" in Briefings in Bioinformatics 2015.

Dimitrios Kleftogiannis has defended his Ph.D. thesis

1 min read ·
Dimitrios Kleftogiannis has defended his Ph.D. thesis "Identification of enhancers in human: Advances in computational studies."

Filling in the blanks of virtual cities

1 min read ·
New software generates three-dimensional models of buildings, with applications for disaster relief support.

CEMSE Big Data Open Day shows off fascinating discoveries

4 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

SFB Group graduated its third PhD student - Dr. Ibrahim Alabdulmohsin!

1 min read ·
Congratulates the SFB group on graduating its third Ph.D. student, Dr. Ibrahim Alabdulmohsin.

Building SANDS at KAUST

2 min read ·
​​​​​We live in a connected world where networked systems play an increasingly important role. These systems, which are the foundational pillars of our modern digital lives, are the result of some remarkable technological advancements and progress in computer science over the past three decades.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

3 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Cocoon finally out: Professor Marco Canini together with VMware, Samsung, and Microsoft craft the ultimate SDN ally

2 min read ·
With the recent emergence of software-defined networking, which brings programmability and lowers the barrier for new functionalities into networks, the academic and industry communities have become very interested in the problem of network verification.

Facebook AI new language explained by KAUST Professor Xiangliang Zhang

3 min read ·
That is not a Messenger troll, but the actual conversation between two chatbots by the Facebook AI Research (FAIR). The two robots, Bob and Alice, were thought the art of negotiation apples and books. They had been instructed to work out how to negotiate between themselves and improve their bartering as they went along. But, after leaving the pair alone, they start talking in this uncompressible but yet effective vocabulary.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Ph. D. Student Khalil Elkhalil nominated finalist for the Best Student Paper Award in the IEEE MLSP Conference

2 min read ·
Fast-placing Machine Learning (ML) is continuously in need of new algorithms, but testing their effectiveness is utterly time-consuming. "Asymptotic Performance Of Regularized Quadratic Discriminant Analysis Based Classifiers," a new paper presented by CEMSE Ph.D. Student Khalil Elkhalil puts ML on the fast gear and a finalist for the Best Student Paper Award in the IEEE MLSP Conference held last September in Roppongi, Tokyo, Japan.

Alumni Feature: Arturo Magana Mora

1 min read ·
CBRC Ph.D. graduate Arturo Magana Mora is currently a postdoctoral researcher at the prestigious National Institute of Advanced Industrial Science and Technology (AIST) in Tokyo, Japan.

Siqing Hou defends his Master’s thesis

1 min read ·
Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

Alumni Feature: Abdullah Khamis

1 min read ·
Abdullah Khamis has had quite an impressive career so far. After graduating with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao, he now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia.

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Maxat Kulmanov defends his thesis proposal

1 min read ·
Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

Our paper entitled "Practical and Dynamic Buffer Sizing using LearnQueue" has been accepted for publications at IEEE Transactions on Mobile Computing.

1 min read ·
Our paper entitled "Practical and Dynamic Buffer Sizing using LearnQueue" has been accepted for publications at IEEE Transactions on Mobile Computing.

New Book "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" of Professor Moshkov and His Former Students is Published by Springer

1 min read ·
A new book - "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" by Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov - was recently published by Springer.

Alumni Focus: Ahmed Abdulmajeed Alabdulkarim, M.S. '11

1 min read ·
Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

KAUST partners with McLaren Racing on R&D

1 min read ·
McLaren Racing and KAUST signed a five-year research and development (R&D) agreement focused on extreme performance technology. The partnership aims to advance research in the areas of computational fluid dynamics (CFD), machine learning, fuels and lubricants, advanced mathematics and sensors and electronics. "This partnership underlines our commitment to supporting global STEM advancements," said Jonathan Neale, chief operations officer of McLaren Group. "KAUST is a leading research and education institution in these fields and mirrors our core values. McLaren Racing is part of a world

Mona Alshahrani defends her thesis proposal

1 min read ·
Mona Alshahrani, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended her thesis proposal on October 10, 2018.

Computing solutions for biological problems

2 min read ·
Xin Gao’s research is building bridges between computer science and biology.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Sara Althubaiti defends her Master's thesis

1 min read ·
Sara Althubaiti, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 5, 2018.

Tweaking tools to track tweets over time

1 min read ·
Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.

Student Focus: Adel Bibi

1 min read ·
KAUST Ph.D. student Adel Bibi, who works on bridging the gap between theory and practice in deep learning, believes we're living in a very exciting time.

Putting the sense in materials

1 min read ·
An interdisciplinary initiative is helping KAUST be at the forefront of a digital revolution, where sensors can find a use just about anywhere.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Researchers Are the First to Generate Images of Realistic and Highly Detailed Texture Maps of Gigapixel in Size Using Deep Neural Networks

1 min read ·
KAUST researchers Anna Fruehstueck, Dr. Ibraheem Alhashim, and Prof. Peter Wonka have developed a novel technique to generate images of realistic and highly detailed texture maps using deep neural networks. The texture images synthesized by their system TileGAN can be of gigapixel size and are created by seamlessly merging smaller texture blocks into a single large image. The underlying neural networks are trained using high-resolution images such as detailed satellite imagery, maps and famous paintings.

Saudi Summer Internship (SSI) Poster Competition Winners

1 min read ·
Rawan Al Ghamdi, Abdulwahab Felemban and Abdullah Zayat were interns this summer as part of the Saudi Summer Internship (SSI) Program. This is a program involving undergraduates from various universities in the Kingdom and this year hosted 43 students. Abdulwahab and Abdullah worked on enhancing ultrasonic indoor localization and in particular using machine learning to classify arriving signals between line-of-sight and multi-path. Abdulwahab and Abdullah won the second place in the poster competition that took place at the end of the SSI program. Rawan Al-Ghamdi was an intern this summer as

ISL Student Khalil ElKhalil does internship at Duke University

1 min read ·
ISL PhD student Khalil ElKhalil was a visiting Duke university from September 2018 to March 2019. Khalil was working with Vahid Tarokh on topics related to random matrix theory, machine learning and statistical finance.

Testing the blind spots in artificial intelligence

1 min read ·
Understanding the situations when artificial intelligence can fail is critical for application of future autonomous vehicles and medical diagnostics.

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

4 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Less chat leads to more work for machine learning

2 min read ·
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

LD Motif Finder locates ancient hidden protein patterns

1 min read ·
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

AI uses your imagination

1 min read ·
The psychology of human creativity helps artificial intelligence imagine the unseen.

Approximating a kernel of truth

1 min read ·
Machine learning tasks using very large data sets can be sped up significantly by estimating the kernel function that best describes the data.

Flattening the sentimental curve

2 min read ·
-By Meres J. Weche, KAUST News As municipal, regional and national governments have mandated longer and more restrictive isolation time periods to slow down the spread of the COVID-19 pandemic and "flatten the curve," KAUST Associate Professor of Computer Science Xiangliang Zhang works on flattening the "emotional curve." Zhang's research focuses on developing reliable algorithms to analyze complex data sets through machine learning and artificial intelligence. Her research group specifically uses computer models to analyze social media posts on Twitter to study people's interests. When the

Peeling back the layers of deep machine learning

1 min read ·
A layer-based approach raises the efficiency of training artificial intelligence models.

Tracking the Curve: Analyzing the Emotional Response to COVID-19

1 min read ·
Hashtags like #covid19 and #coronavirus help us stay up to date on the developments of the new coronavirus pandemic. But beyond breaking news, tweets also offer a glimpse into the emotional side of the COVID-19 crisis.

Meet KAUST prospective student: Matheus Vinícius Medeiros de Souza

2 min read ·
Matheus Vinícius Medeiros de Souza is a 24-year-old electrical engineering graduate from the city of Natal, Brazil. Matheus first came to KAUST as a visiting student in 2019 and was instantly impressed by the university’s research facilities.

Meet KAUST Prospective Student: Xinge Yang

1 min read ·
Xinge Yang is a 22-year-old graduate who will join KAUST from the University of Science and Technology of China (USTC). Yang will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich.

KAUST Prospective Student: Damilya Saduakhas

2 min read ·
Damilya Saduakhas, 21 years old, from Kazakhstan, obtained a B.Sc. degree in mathematics from the Nazarbayev University, Kazakhstan. Damilya has been selected as a young scientist to participate in the summer Data Lab from Yessenov Foundation and will join KAUST in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics Research group under the supervision of Professor David Bolin.

Meet KAUST Prospective Student: Igor Sokolov

2 min read ·
Igor Sokolov, 23, is an applied mathematics and physics graduate who will join KAUST from the Moscow Institute of Physics and Technology, Russia. Igor first came to KAUST as a visiting student in 2019 and was highly impressed by the University's living and working facilities. It was this first impression that prompted him to pursue an M.S. at KAUST. Sokolov will join KAUST in the Fall of 2020 as an M.S. candidate under the supervision of Professor Peter Richtarik.

KAUST prospective student: Xiaochuan Gou

2 min read ·
Xiaochuan Gou, 27, from Dalian, China, will join KAUST in the fall of 2020. He will join KAUST as a Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

KAUST prospective student: Qizhou Wang

1 min read ·
Qizhou Wang, 22, is a communication engineering graduate who will join KAUST from the University of Electronic Science and Technology, China. Wang will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate in the KAUST Primalight Lab under the supervision of Professor Andrea Fratalocchi.

Meet KAUST prospective student: Zhenwen Liang

1 min read ·
Zhenwen Liang, 22, is a computer science graduate from the University of Electronic Science and Technology of China (UESTC), China. Zhenwen will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

Meet KAUST prospective student: Pratik Nag

1 min read ·
Pratik Nag will join KAUST this fall as a Ph.D. candidate and member of the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Pratik holds a master's degree in statistical quality control and operation research, and a bachelor's degree in statistics, from the Indian Statistical Institute and the University of Calcutta, India.

Computers excel in chemistry class

1 min read ·
Machine learning models can rapidly and accurately estimate key chemical parameters related to molecular reactivity.

Meet KAUST prospective student: Kai Yi

1 min read ·
Kai Yi is a graduate from Xi'an Jiaotong University (XJTU), China, who will join KAUST in the fall of 2020. Kai will join KAUST as a M.S./Ph.D. candidate and member of the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. KAUST's high reputation and Kai's interest in computer vision and machine learning prompted him to choose the University to further his academic career.

Meet KAUST prospective student: Sahar Ammar

1 min read ·
Sahar Ammar will join KAUST this fall as a M.S./Ph.D. candidate and member of the Networking Research Lab and Communication Theory Lab under the co-supervision of Professor Basem Shihada and Professor Mohamed-Slim Alouini. The multidisciplinary engineering graduate will join the University from Tunisia Polytechnic School (TPS).

Meet KAUST student: Konstantin Burlachenko

1 min read ·
Konstantin Burlachenko holds a master’s degree in computer science from Bauman Moscow State Technical University (BMSTU), Russia. After graduating from BMSTU, he worked in several prominent Moscow companies, such as Huawei, NVIDIA, and Yandex. He joined KAUST in August 2020 as a Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab.

Meet KAUST prospective student: Reem Alghamdi

1 min read ·
Reem Alghamdi is a computer science graduate from Princess Nourah bint Abdulrahman University, Saudi Arabia. Reem will join KAUST this fall as a M.S. candidate under the supervision of Professor Peter Richtarik.

Meet KAUST student: Grigory Malinovsky

2 min read ·
Grigory Malinovsky joined KAUST this fall as a M.S./Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab. Grigory holds a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Malinovsky first came to KAUST as a visiting student and was instantly impressed by the research facilities and working atmosphere. His outstanding internship experience was a catalyst in choosing KAUST as the next step in his academic career.

KAUST Environmental Statistics group publishes book exploring multivariate statistical analysis

2 min read ·
The KAUST Environmental Statistic (ES) research group has recently published a book titled "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches." The book is the outcome of the Core Research Grant (GRG) project led by KAUST Professor Ying Sun, Associate Professor of Statistics.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Professor Ghanem wins 2020 Abdul Hameed Shoman Award for Arab Researchers

3 min read ·
Bernard Ghanem, KAUST associate professor of electrical and computer engineering and computer science, recently received the 2020 Abdul Hameed Shoman Award for Arab Researchers for ''Machine Learning and Big Data.''

Machine Learning Arabic NLP webinar

1 min read ·
Join us to learn, compete, and win at the Machine Learning Webinar and learn more about participating in the Arabic NLP challenge! Register Now!

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

3 min read ·
KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

Broadening horizons for people with quadriplegia

1 min read ·
An assistive technology uses magnetic skin to support freedom of movement for people with quadriplegia.

Xin Gao promoted to rank of professor

1 min read ·
Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

New paper published in JRSS Series A

1 min read ·
The paper Functional ANOVA modelling of pedestrian counts on streets in three European cities by David Bolin, Vilhelm Verendel, Meta Berghauser Pont, Ioanna Stavroulaki, Oscar Ivarsson, and Erik Håkansson has been published in the Journal of the Royal Statistical Society Series A.

Machine learning at speed

1 min read ·
Optimizing network communication accelerates training in large-scale machine-learning models.

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

CBRC congratulates Aryam Alotaibi a SRSI student on winning the top paper Award

1 min read ·
Aryam Alotaibi a SRSI student at CBRC wins the top paper Award titled "Computational method to predict drug-disease associations using machine learning and graph embedding."

Drop the zeros for a performance boost

1 min read ·
An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

KAUST paper wins 2020 Computational Optimization and Applications Best Paper Award

1 min read ·
KAUST Professor of Computer Science Peter Richtárik and his former student Nicolas Loizou, currently a postdoctoral researcher at Mila - Quebec Artificial Intelligence Institute and soon to take up an assistant professorship position at Johns Hopkins University, recently received the 2020 Computational Optimization and Applications (COAP) Best Paper Award.

Deeper defense against cyber attacks

1 min read ·
To address the growing threat of cyberattacks on industrial control systems, a KAUST team including Fouzi Harrou, Wu Wang and led by Ying Sun has developed an improved method for detecting malicious intrusions.

Hybrid neural networks for real-time electromagnetic wellbore casing inspection

2 min read ·
A cluster of hybrid neural networks enables the precise characterization of metal loss on concentric wellbore casings.

Fault Identification of Photovoltaic Array Based on Machine Learning Classifiers

1 min read ·
The Solar Photovoltaic (PV) industry has experienced significant growth over the past years due to the technology’s clear economic and environmental benefits. The world’s net electricity generation from grid-connected PV systems is expected to rise. Although PV systems don’t incorporate moving parts and usually require low maintenance, they are still subjected to diverse faults across the various system components. Proper fault detection and/or identification is thus necessary to avoid significant energy generation loss and large capital expenditures. Fault identification in Photovoltaic array

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

2 min read ·
KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Can machine learning help predict disease spread?

1 min read ·
Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

MS Thesis Defense: Rare variant collapsing analysis on UK Biobank by Yang Liu

1 min read ·
Yang Liu, M.S./Ph.D. candidate in the Bio-Ontology research group under the supervision of Professor Robert Hoehndorf successfully defended her thesis "Rare variant collapsing analysis on UK Biobank".

MS Thesis Defense: Description Logic EL++Embeddings with Intersectional Closure by Xi Peng

1 min read ·
Xi Peng MS Student under the supervision of Professor Robert Hoehndorf successfully defended his thesis "Description Logic EL++Embeddings with Intersectional Closure"

Prof. Hailiang Liu visits our group

1 min read ·
Prof. Hailiang Liu from Iowa State University will be visiting our group during May.

Computer learns to write captions with feeling

1 min read ·
Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

AI helps diagnose post-COVID lung problems

1 min read ·
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

KAUST alumnus named in Forbes' “30 Under 30 Asia list” – Class of 2022

3 min read ·
KAUST alumnus Yu Li (Ph.D. '20) was recently named amongst the Forbes' "30 Under 30 Asia list" – Class of 2022. Forbes recognized the current assistant professor of computer science and engineering at The Chinese University of Hong Kong (CUHK) for his work developing algorithms to solve problems in biology and health care.

Extreme analysis of seizures

1 min read ·
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Course: A Mathematical Introduction to Deep Learning

1 min read ·
I will give an elementary introduction of basic deep learning models and training algorithms from a mathematical viewpoint. In particular, I will relate some basic deep learning models with finite element and multigrid methods. I will also touch on some advanced topics to demonstrate the potential of new mathematical insight and analysis for improving the efficiency of deep learning technologies and, in particular, for their application to numerical solution of partial differential equations. Course Schedule View the full course schedule here.

Summer Internships at SENTRY

1 min read ·
SENTRY lab hosted three Saudi Summer Internship Students from US universities to collaborate on our current research projects.

Steering Customized AI Architectures for HPC Scientific Workloads: A Bird of Feather at SC22!

2 min read ·
Abstract: We explore the possibilities of a hybrid system capable of solving both HPC and AI scientific problems. Such a hybrid architecture combines the synergism between classical HPC platforms and dedicated AI chip systems, which is important due to the computational challenges brought to the fore by massively parallel Exascale systems. We discuss the system functionality, the algorithmic software adaptations, and performance considerations. We present efforts in supporting AI/ML applications in addition to seismic imaging, climate/weather prediction, and computational astronomy on hybrid

New preprint available from research on quasi-Newton methods for stochastic optimization

1 min read ·
A preprint of a new research project of our group named " Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization" is available at arXiv. The manuscript is authored by André Carlon, Prof. Luis Espath, and Prof. Raúl Tempone. Abstract: Using quasi-Newton methods in stochastic optimization is not a trivial task. In deterministic optimization, these methods are often a common choice due to their excellent performance regardless of the problem's condition number. However, standard quasi-Newton methods fail to extract curvature

2022 SPE Annual Technical Conference and Exhibition

3 min read ·
Moutazbellah Khater and Tarek Mostafa participated in SPE Annual Technical Conference and Exhibition (ATCE) from 3-5 October 2022 in Houston, Texas. This flagship event is the highest caliber technical event for E&P professionals from around the globe. It was a great opportunity for Moutazbellah & Tarek to present their latest research papers where they pushed the limits of well-integrity and pipelines inspection to a new level. Moutazbellah presented his paper entitled “Remote Field Eddy Current System Using Three Axis Fluxgate Magnetometer for Corrosion Inspection” while Tarek presented two

BORG team win at the KAUST-NVIDIA GPU Hackathon 2022

2 min read ·
Congratulations to Senay Kafkas, Maxat Kulmanov and Fernando Zhapa Camacho of the Bio-Ontology Research Group (BORG) on winning at the KAUST-NVIDIA GPU Hackathon 2022. The 8th edition of the Hackathon on Accelerating Scientific Applications using Graphical Processing Units (GPUs) was held at King Abdullah University of Science and Technology (KAUST), twelve teams of students and researchers enjoyed the opportunity to accelerate their codes in GPU’s under the guidance of expert mentors of NVIDIA, Codee, KAUST Supercomputing Lab (KSL) and KAUST core labs.
Montserrat Ramirez de Angel receiving the Best Pitch Award at IEEE 2024 Sensors Conference

Giving voice to the voiceless through assistive technology

2 min read ·
Artificial skin coupled with sensors and machine learning technology allows people with voice disorders to communicate using soundless mouth movements.

Zhize Li

Research Scientist, Computer Science
Zhize Li is a Research Scientist working with Professor Peter Richtarik in his research group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Zhize Li received his Ph.D. in Computer Science from the Institute for Interdisciplinary Information Sciences of Tsinghua University in 2019 under the supervision of Prof. Jian Li. On the career side, before joining KAUST, he was a visiting scholar at the Computer Science Department of Duke University (Hosted by Prof. Rong Ge) and a visiting scholar at the Industrial and

Zhuo Yang

Ph.D. Student, Computer Science
Zhuo Yang is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Zhuo's research interests include machine learning and data mining. Education Profile ​B. E., Computer Science, Nankai University, China, 2014. M. E., Computer Science, Nankai University, China, ​2017.

CEMSE New Faculty: Professor Rolf Krause

3 min read ·
Professor Rolf Krause, our new faculty member at KAUST, drives sustainable progress across various domains, from medicine to geology, with his pioneering research in applied mathematics and efficient algorithms.

Zhenwen Liang

Ph.D. Student, Computer Science
Zhenwen Liang is a M.S./Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Zhenwen obtained a bachelor's degree in computer science from the University of Electronic Science and Technology of China (UESTC), China. Research Interests Zhenwen's research interests include AI research and machine learning.

Zhenzhen Zou

Zhenzhen Zou worked as a ​Researcher at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Zhenzhen's research interests included Bioinformatics and Machine learning.

Zihang Xiang

Ph.D. Student, Computer Science
My current research is on privacy-preserving data analysis. I am interested in pushing the boundaries of differential privacy via principled approaches in broad machine learning applications.

Yuxi Hong

Ph.D. Student, Computer Science
Yuxi Hong is a PhD student working with Professor David Keyes. He is working on accelerating the training process of deep learning from different aspects. Why KAUST? KAUST is a great place for research. Why HPC and machine learning? I believe we need more computation in the future. Scalable and efficient computing methods are basic tools for the scientific research. Education Profile MS Tsinghua University. BS Tsinghua University.

Zainab A. Homoud

M.S. Student, Bioengineering
Research Interest Studying : Brain time series, fMRI, EEG, Signal Processing and Machine Learning for better Mental Disease Diagnosis and Consciousness Study. Previous Education Undergraduate study (B.S) in Biomedical Engineering and Health Science at the Joint program of North Carolina State University and UNC Chapel Hill.

Yulian Wu

Ph.D. Student, Computer Science
Yulian Wu is a Ph.D. candidate under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST).

Yibo Yang

Former members
Yibo Yang is a research scientist at the Image and Video Understanding Lab (IVUL) under the supervision of Professor Bernard Ghanem. Before joining KAUST, he obtained his Ph.D. from Peking University in 2021. He worked as a researcher at JD Explore Academy, JD.com, in 2021-2023.

Youssef S. Mohamed

Ph.D. Student, Computer Science
Education Youssef Mohamed is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Bachelor's Degree in Communication and Information Engineering, University of Science and Technology in Zewail City, Cairo, Egypt (2013-2018) Master's Degree in Computer Science, University of Tartu, Tartu, Estonia (2019-2022) Research Interests I am interested in developing AI that is aware of the Emotional and Cultural aspects of human intelligence. We study human

Yuchen Li

Ph.D. Student, Computer Science
Yuchen Li is a PhD Student under the supervision of Professor Mohamed Elhoseiny in Vision-CAIR Group at Visual Computing Center at King Abdullah University of Science and Technology (KAUST). Education and Early Career Before joining KAUST, Yuchen was a research intern at iFLYTEK, an intelligent speech and artificial intelligence company in Hefei, China, for three months. He was a Rocket MQ open source contributor and Alibaba summer of code student developer with rich research and engineering experience. He visited NUS, UBC, THU, and other prestigious universities for academic exchanges with a

Yipeng Qin

Postdoctoral Research Fellow, Computer Science
Yipeng Qin was a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Currently, he is a Lecturer at the School of Computer Science and Informatics in Cardiff University (CU), United Kingdom. Education and Early Career In 2013, Yipeng Qin graduated with a Bachelor of Engineering in Electronic Engineering from Shanghai Jiao Tong University in China. Later during 2017, he obtained his Ph.D. in Computer Science from the National Centre for Computer Animation, Bournemouth University in

Yidan Zheng

Ph.D. Student, Computer Science

Yu Li

Ph.D. Student, Computer Science
Research Interests Yu Li is a Ph.D. student at KAUST, majoring in Computer Science. His advisor is Prof. Xin Gao. His research interests are Deep Learning, Machine Learning, and Bioinformatics. He got his Master degree in Computer Science at KAUST in December 2016. Before that, he got his Bachelor degree in Biosciences at the University of Science and Technology of China (USTC). Scientific and Professional Membership Association for the Advancement of Artificial Intelligence(AAAI) Member International Society for Computational Biology (ISCB) Member Education Profile Ph.D student, Computer

Yazeed Alharbi

Yazeed Alharbi is a Ph.D. candidate under the supervision of Prof. Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career In 2015, Yazeed obtained his bachelor degree from Purdue University in the computer graphics and visualization track, with a minor in philosophy. In 2018, he received his master degree from King Abdullah University of Science and Technology (KAUST). He mainly learned about computer vision and the process of publication in that field. Research Interest Currently, Alharbi research is focused

Yang Liu

Ph.D. Student (former), Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

Yahya Bokhari

Postdoctoral Research Fellow, Computer Science
Yahya Bokhari is a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST), working with Professor Wolfgang Heidrich on enhancing X-ray images under Smart Health Initiative. Education and Early Career Yahya Bokhari received his bachelor's degree in Medical Technology from King Abdulaziz University, KSA. He holds a Master's degree in Bioinformatics and a Ph.D. degree in Computer Science from Virginia Commonwealth University, USA. Research Interest Dr. Yahya Bokhari is mainly interested in Image Processing, Computer Vision

Xun Qian

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Xun Qian is a Postdoctoral Research Fellow at the Visual Computing Center (VCC), King Abdullah University of Science and Technology (KAUST), working with Professor Peter Richtárik in his research group. Education and Early Career Dr. Qian obtained his B.Sc. degree in Mathematics from Huazhong University of Science and Technology, China, in 2013. In 2017, he has obtained a Ph.D. degree in Mathematics from Hong Kong Baptist University (HKBU), Hong Kong. Research Interest Xun Qian is interested in stochastic methods and algorithms, machine learning and interior-point methods. Selected

Xiangyu Cui

I will enjoy the real fun of scientific research in KAUST. There are still many people who don’t have good access to internet.

Xin Gao

Professor, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaochuan Gou

Ph.D. Student, Computer Science

Xi Peng

Ph.D. Student (former), Computer Science
Xi Peng likes pop music and doing sports. He graduated from SUSTech in 2021 and planning to continue his academic career at KAUST under the supervision of Professor Robert Hoehndorf. "He says KAUST has the best environment to do research. Life in KAUST is also comfortable. I believe I can achieve a lot in KAUST". Research Interest applications of machine learning and logic integration in biology Education Profile B.Sc, Computer Science, Southern University of Science and Technology, Shenzhen, China, 2017-2021 M.Sc, Computer Science, King Abdullah University of Science and Technology, Thuwal

Waqas Waseem Ahmed

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Erasmus Mundus Joint Doctorate in Photonics Engineering, Universitat Politècnica de Catalunya (Spain), Universita degli Studi di Firenze (Italy), Karlsruhe Institute of Technology (Germany), Université Paul Cèzanne Aix Marseille III (France).(2018) MS in Electrical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. (2014) BS in Electronic Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI), Pakistan. (2009) Research Interests Non-Hermitian Photonics, Nonlinear Optics, Machine Learning, Laser Physics.

Waleed Abu Laban

Software Developer, Repli5
Waleed Abu Laban is a M.S./Ph.D. candidate at the KAUST Communication and Computing Systems Lab under the supervision of Professor Ahmed Eltawil. Before joining KAUST, Waleed obtained a bachelor's degree in electrical engineering from The University of Jordan. Research Interests Waleed's research interests include signal processing and machine learning.

Trisevgeni Rapakoulia

Trisevgeni Rapakoulia obtained her Ph.D. degree (2016-2019) in Computer Science under the supervision of Professor Xin Gao at structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Trisevgeni's research interests include bioinformatics, System Biology, Prediction of harmful SNPs, Genome-Wide Association Studies and Artificial Intelligence, Machine Learning Approaches, Evolutionary Algorithms, Feature Selection Techniques. Research field: study the effects of drugs and drug combinations in the transcriptome level

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Uchenna Akujuobi

Uchenna Akujuobi obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). Research interests Uchenna's research interests included Machine learning, data mining, Social Networks, Graph mining, Information Retrieval, Text Mining, Big Data, and Deep Networks. Ph.D. dissertation entitled “Learning from Scholarly Attributed Graphs for Scientific Discovery”​. First employment: Research Scientist, Sony AI​, Tokyo, Japan. He is also the creator of Delve, a dataset retrieval, and

Ussama Zahid

Ussama Zahid was an M.S. student in the Accelerated Connected Computing Lab at KAUST. He works at the intersection of deep learning and embedded systems, i.e., “Edge AI”. He specializes in efficient training and inference of quantized deep neural networks (DNNs) and hardware-software co-design of DNN accelerators. Before joining KAUST, he was as a research engineer intern at Xilinx Research Labs, Dublin Ireland, Ireland where he worked on quantized and robust training of Deep Neural Networks (DNNs), resulting in patents and publications. He received his Bachelor's degree in Electrical

Tarek H. Mahmoud

Areas of Expertise and Research Interests ​Computer Vision Machine Learning Robotics Education Profile ​MS in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2017) ​​BS in Electrical Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia (2016)

Tilman Hinnerichs

Visiting Student (former), Bio-Ontology Research Group
Tilman Hinnerichs was a visiting student at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Research Interests Tilman's research interests included ​Machine Learning, Bioinformatics, Ontologies, Reasoning, Logic, Particle Methods. He was particularly interested in the application of machine-learning algorithms to problems of the bioscience field. Education Profile ​Since 2016: Diploma Student, Computer Science, TU Dresden​. 2015-2016: Student, Human Medicine, Charité Berlin.

Talha Ariff

Talha is studying the use of novel fiber-optic distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) as a tool to help monitor and control anthropogenic acoustic pollution in marine environments.

Su Wang

Su Wang obtained a master's degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory in King Abdullah University of Science and Technology (KAUST). First employment: Didi Chuxing​, Beijing, China, 2016. Research Interests Su's research interests included ​Data mining and machine learning. Education Profile ​​B.Sc., Information Science & Electronic Engineering, Zhejiang University, 2014. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST). Awards and Distinctions First Prize

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Slavomir Hanzely

Slavomir Hanzely is an MS/Ph.D. student in the Applied Mathematics and Computational Science Program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Slavomir earned his Bachelor's Degree in Informatics with a background in both Mathematics and Computer Science from the Faculty of Mathematics, Physics and Informatics University (FMPH UK), Slovakia in 2019. Research Interest He is interested in Machine Learning, Federated Learning, and Stochastic Algorithms. Education

Souhaib Ben Taieb

Postdoctoral Research Fellow, Statistics
Dr. Souhaib Ben Taieb is an Assistant Professor in Computer Science at University of Mons (UMONS) in Belgium. He is also an Adjunct Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. From 2016 to 2018, Souhaib was a Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. In 2015, he was a postdoctoral research fellow in the Spatio-Temporal and Data Science Group at KAUST. He was a visiting scholar at several international institutions, including the School of Earth

Shichao Pei

Ph.D. Student, Computer Science
Shichao Pei is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Shichao Pei research interests include machine learning and data mining. Education Profile ​B. E., Software Engineering, Xi'an Jiaotong University​, 2016.

Shahid Hussain

Ph.D. Student, Computer Science
Research Interests Design and analysis of decision trees based on dynamic programming and greedy approaches Machine learning Discrete optimization Selected Publications Azad, M., Chikalov, I., Hussain, S., and Moshkov, M. Restricted multi-pruning of decision trees, In Machine Learning and Data Analytics Symposium as plenary talk (2016) (Awarded best paper). Busbait, M., Chikalov, I., Hussain, S., and Moshkov, M. Diagnosis of constant faults in read-once contact networks over finite bases. Discrete Applied Mathematics, 184 (2015), pp. 76–88. AbouEisha, H., Hussain, S., Lozin, V., Monnot, J

Shahzad Gishkori

Postdoctoral Research Fellow (former), Information Science Lab
Postdoctoral Research Fellow, Electrical and Computer Engineering , King Abdullah University of Science and Technology.

Shahad Qathan

M.S. Student (former), Computer Science
Shahad Qathan is an MS candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Artificial Intelligence, more specifically Machine Learning and its applications in healthcare and Bioinformatics Education Profile B.Sc, Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, 2020

Shariq Farooq Bhat

M.S. Student, Computer Science
Shariq Farooq Bhat is an MS/PhD student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). Education and Early Career Shariq Farooq Bhat obtained his bachelor of technology degree in Electronics & Communication Engineering from the National Institute of Technology Srinagar, India in 2018. During bachelor’s degree, he was an intern at Texas Instruments (TI) Center for Embedded Product Design at NSIT in New Delhi. Then, in 2016, he worked at the Indian Institute of Science (IISc) as a computer vision and deep learning research Intern and

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Shasha Liu

Shasha Liu is a visit student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Shasha Liu will obtain her bachelor degree in Information Countermeasure Technology from University of Electronic Science and Technology of China (UESTC) in 2022. Scientific Interest Shasha Liu is interested in Machine Learning and Communicate Engineering. Education Profile B.S. in Information Countermeasure Technology, University of Electronic Science and Technology of China (UESTC)

Sara Althubaiti

Ph.D. Student (former), Computer Science
Sara Althubaiti is currently a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Salman Alsubaihi

Salman Alsubaihi was an Electrical and Computer Engineering Master Student in the Image and Video Understanding Lab (IVUL) under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Salman Alsubaihi obtained his bachelor degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in Saudi Arabia in 2017. In 2019, he received his Master in Electrical Engineering from KAUST. Research Interests ​ Salman is focusing on understanding deep neural

Sakhaa Alsaedi

Ph.D. (former), Computer Science

Sahar Ammar

Sahar Ammar’s research focuses on network slicing in integrated terrestrial and non-terrestrial networks.
Sameh Abdulah

Sameh Abdulah

Dr. Sameh Abdulah specializes in HPC, scalable algorithms, and climate modeling, with innovations receiving the ACM Gordon Bell Prize for Climate Modelling.
Sabine El Khoury

Sabine El Khoury

Sabine El Khoury is an instructional professor of applied mathematics and computational sciences specializing in commutative algebra.

Ruslan Zhagypar

MS/PhD Student, Electrical and Computer Engineering , King Abdullah University of Science and Technology

Rolf Krause

Professor Rolf Krause is an experienced researcher and educator specializing in designing and analyzing algorithms to solve complex problems in scientific computing and machine learning.

Riyadh Alghamdi

Research Interests My research interests lie in the intersection of software engineering, machine learning, and cyber-physical systems, with a focus on their applications in the oil and gas industry. I am particularly interested in developing intelligent systems that can optimize the extraction process and improve safety in the industry. My work involves using a range of programming languages such as MATLAB, Python, C, and C++ to develop and implement algorithms for machine learning and microprocessor interfacing. My goal is to contribute to the development of more efficient and sustainable

Reema Alnafisi

M.S. student, advised by Professor Tareq Al-Naffouri under the Information System Lab (ISL). at King Abdullah University of Science and Technology (KAUST)

Ricardo Henao

Associate Professor (former), Bioengineering
​Ricardo Henao, a quantitative scientist, is an Associate Professor of Bioengineering in the Biological and Environmental Science and Engineering ( BESE) Division, and Computer Science in the Computer, Electrical, Mathematical Sciences and Engineering Division ( CEMSE), member of the Smart Health Initiative ( SHI) and the Computational Biosciences Research center ( CBRC), at KAUST (King Abdullah University of Science and Technology). He is also currently an Associate Professor in the department of Biostatistics and Bioinformatics, Department of Electrical and Computer Engineering ( ECE)

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Ramzan Umarov

Ph.D. Student, Computer Science
Ramzan is a PhD candidate, working on solving biological problems by using applied machine learning with focus on deep learning. He has developed deep learning based methods to solve bioinformatics problems achieving state-of-the-art performance, focusing on various aspects of gene regulation. Umarov obtained his Master degree from Imperial College London, Advanced Computing course. Research Interests The main research interest of Ramzan Umarov is applied machine learning especially Deep Learning. Professional Profile 2011-2012, Programmer, Softberry, Mount Kisko, NY, USA 2013-2014, Programmer

Rawan Olayan

Rawan Olayan obtained both, her MS Degree and PhD Degree from Computer Science at KAUST in 2013 and 2018 respectively. Her master thesis and PhD thesis supervisor was Vladimir Bajic. Under the supervision of her advisor Professor Vladimir Bajic, she completed her thesis on developing novel computational methods to predict drug-target interactions and their functional effects using data integration, graph mining and machine learning approaches. Currently, Rawan is working as a postdoctoral associate in the Carter Lab at the Jackson Laboratory for Genomic Medicine in Farmington, Connecticut. In

Rahaf Shiqdar

Visiting Student (former), Bio-Ontology Research Group
Rahaf Shiqdar was a visiting student from ​Umm Al-Qura University ​at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Rahaf's research interests included ​Bioinformatics, Machine and Deep learning.

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Raphael Bridi

Ph.D. Student, Electrical and Computer Engineering ,King Abdullah University of Science and Technology (KAUST).

Rameen Abdal

Ph.D. Student, Computer Science
Rameen Abdal is a Master student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). He has graduated in 2023. Education and Early Career Rameen Abdal obtained his bachelor of technology degree in Electronics and Communications Engineering from the National Institute of Technology in Srinagar, India in 2018. After that, he joined KAUST for the MS/PhD program in computer science to continue his studies. Before joining KAUST, Rameen was an intern at Texas Instruments (TI) Center for Embedded Product Design at Netaji Subhas University of

Peter Wonka

Professor, Computer Science
Professor Peter Wonka is a computer scientist with a research focus in computer vision, computer graphics, remote sensing, and machine learning.

Qizhou Wang

Qizhou Wang research focuses on advancing optical sensing, nanophotonics, and intelligent computational imaging.

Pratik Nag

Ph.D. Student, Statistics
Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Qiming Fang

Prof. Fang is a visiting professor from Hangzhou Dianzi University, China at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Prof. Fang's research interests include Bioinformatics and Machine learning.

Qiannan Zhang

Ph.D. Student, Computer Science
Qiannan Zhang is a Ph.D. student in Computer Science program at Machine Intelligence and Knowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). ​​​​​Research interests Qiannan's research interests include Machine learning and data mining​. Education Profile B. E., Communication Engineering, Tianjin University, China, 2010-2014. M. E., Communication Engineering, Tianjin University, China, 2014-2017. Research Intern, KAUST, 2017.

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Peihao Zhu

Ph.D. Student, Computer Science
Peihao Zhu is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. He has completed his Ph.D and graduated in 2023. Education and Early Career Peihao Zhu obtained his bachelor of engineering in Automation from Northeastern University in China in 2016. After that, he continued his studies in Computer Science and received his master's degree from King Abdullah University of Science and Technology (KAUST) in Saudi Arabia in 2019. Research Interest

Peng Yang

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Yang worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Peng is a research scientist at Baidu Research. Research Interests ​Machine Learning and Data Mining problem, especially online active learning, multi-task learning, recommender systems, and deep learning. Algorithms for bioinformatics problems, especially in disease gene prediction, drug-target interaction prediction, protein complex, and microbial community pattern detection

Peihao Li

Olaa A. Motwalli

Research Interests Olaa Motwalli is a Ph.D. student in Computational Science and a member of the Computational Bioscience Research Center (CBRC) at KAUST. She obtained her Bachelor's degree in Computer Science at King Abdul Aziz University in Jeddah and obtained her Master's degree in Computer and Software Engineering at Widener University in the USA. Her research interests are mainly the development and computational support for the design of sustainable biofuel production via microbial cell factories through machine learning, graph mining, and algorithm development, genome-scale metabolic

Omar Mena Facio

M.S. Student, Computer Science
Omer Mena Facio is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Omer Mena Facio

Olga Mashkova

Ph.D. Student, Computer Science
PhD student in Computer Science. Research interests: machine learning, bioinformatics, mathematics, knowledge representation and reasoning, deep learning, algorithms.

Nurislam Tursynbek

Visiting Student (former), Bio-Ontology Research Group
Nurislam Tursynbek was a visiting student at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Nurislam's research interests included machine learning and computational biology.

Noor Hussein

Noor Hussein is a dedicated learner in Machine Learning and Artificial Intelligence. She earned her undergraduate degree in Applied Mathematics and Statistics from Khalifa University (KU) in Abu Dhabi, UAE, with the highest honors. In the fall of 2022, Hussein embarked on her academic journey by joining the Master's program in Machine Learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi, UAE. She is expected to graduate from MBZUAI in the summer of 2024. During the summer of 2023, Hussein got the opportunity to join as a Visiting student at King Abdullah

Noor Bafageeh

Noor Bafageeh was a Master student in Computer Science Program under the supervision of Professor Bernard Ghanem at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Education and Career Noor earned her bachelor degree in Computer Science from Effat University in Jeddah in 2016. She joined KAUST and received her master degree in Computer Science in 2018. Research Interest She is interested in computer vision, machine learning, Generative Adversarial Networks (GAN) and deep learning. Education Profile M.S., Computer Science, King Abdullah University of

Ngan Nguyen

Ph.D. Student, Computer Science
Ngan Nguyen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ngan has a Master's Degree in Computer Science. She worked at Gameloft Company as a Developer. After that, she joined the University of Information Technology, Vietnam National University in Ho Chi Minh City as a Lecturer. Research Interest Ngan is focusing on Computer Graphics, Visualization, and their overlaps into

Norah S. Alballa

Ph.D. Student, Computer Science
Education Profile Ph.D. in Computer Science, CEMSE, KAUST, 2022-present M.S. in Computer Science, Portland State University, 2016 B.S. in Information Technology, King Saud University (KSU), 2010

Mohammed Hussain AlSharif

Postdoctoral Research Fellow, California Institute of Technology (Caltech)
Dr. Mohammed AlSharif is a Postdoctoral Fellow at the California Institute of Technology (Caltech) and a recipient of the KAUST Ibn Rushd Fellowship. His research bridges Riemannian geometry, statistical estimation, and learning for autonomous systems, with applications to high-accuracy pose estimation and robotics.
Mostafa Emara

Mostafa Mohamed Lotfy Emara

Postdoctoral Research Fellow (former), Electrical and Computer Engineering
PhD Student, Electrical and Computer Engineering at KAUST (King Abdullah University of Science and Technology)

Mudassir Masood

Ph.D. degree in electrical engineering from the King Abdullah University of Science and Technology (KAUST). ,Assistant Professor, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

Motasem Alfarra

Motasem Alfarra is an Electrical and Computer Engineering Ph.D. student in Image and Video Understanding Lab (IVUL) Group in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem.

Mona Alshahrani

Ph.D. (former), Computer Science
Mona Alshahrani obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Machine Learning, human bioinformatics, data integration. The combination of neuro-symbolic methods over ontologies and semantic RDF graphs with statistical methods. Their applications in data analysis and features representations in several biomedical applications such as phenotypes prediction, drug repurposing, and others. I'm also interested in network

Mohammed Aldosari

Ph.D. Student, Mechanical Engineering
Education 2021.08 - Present Ph.D. Student in Mechanical Engineering, KAUST, KSA 2018.08 - 2020.06 M.S. in Mechanical Engineering, KAUST, KSA 2011.08 - 2016.05 B.Sc. in Aerospace Engineering, KFUPM, KSA Professional Profile 2022.01 - 2022.05 Teaching Assistant, CEMSE, KAUST, KSA 2017.07 - Present Researcher, Center of Excellence for Aeronautics & Astronautics, King Abdulaziz City for Science and Technology, KSA 2017.04 - 2017.07 Maintenance Planning Engineer, SaudiGulf Airlines, KSA Selected Publications M.N.Aldosari, K.K.Yalamanchi, X. Gao, S.M. Sarathy, "Predicting entropy and heat capacity

Mohammed Aasim Shaikh

MS Degree ,Electrical and Computer Engineering ,King Abdullah University of Science and Technology (KAUST) .

Mohammed Ali Abugurain

Visiting student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Mohammad Mohiuddin Azad

Ph.D. Student, Computer Science
Research Interests Study of greedy and dynamic programming algorithms for construction of exact and approximate decision trees, rules and reducts for decision tables with many-valued decisions Analysis and design of classifiers based on decision trees, reducts, and decision rule systems Data mining, Machine Learning Selected Publications Mohammad Azad, Igor Chikalov, Shahid Hussain and Mikhail Moshkov. “Restricted multi-pruning of decision trees”. MLDAS (Machine Learning and Data Analytics Symposium) 2016 organized by QCRI (Qatar Computing Research Institute) and Boeing International, Doha

Mohammad Tamim Alkhodary

Ph.D. degree in electrical engineering from the King Fahd University of Petroleum and Minerals. Postdoctoral Fellow at École de technologie supérieure, Montreal, QC, Canada

Mohammad Shehab

Postdoctoral Research Fellow, Electrical and Computer Engineering
Education and Early Career Currently, Mohammad Shehab is a postdoctoral Researcher with CEMSE Division, King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Previously, he was with the University of Oulu, Finland, from which he obtained his doctoral degree in 2022. Prior to that, he obtained his B.Sc from Alexandria University in 2011, two M. Sc degrees from the Arab Academy and University of Oulu in 2014 and 2017, respectively. He worked as TA at Alexandria University and the Arab Academy in Egypt from 2012-2015. Shehab was a visiting researcher to TU Dresden, Aalborg

Modar Alfadly

Ph.D. Student, Computer Science
Modar Alfadly is a Ph. D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Alfadly received his bachelor’s degree in Software Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2016. A year later, he obtained his master’s degree in Computer Science and successfully defended his thesis, titled “Analytic Treatment of Deep Neural Networks under Additive Gaussian Noise”. Research Interest Modar Alfadly is focusing on understanding deep neural networks to build more intelligent

Mohamed A Suliman

Visiting Scholar (former), Information Science Lab
MS Degree at King Abdullah University of Science and Technology, Ph.D. candidate, Imperial College London..

Merey Ramazanova

Postdoctoral Research Fellow, Computer Science

Mikhail Moshkov

Professor Moshkov specializes in various areas of discrete applied mathematics, theoretical computer science and operations research. He is the author or co-author of more than 200 research publications, including 10 books published by Springer.

Mher Safaryan

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Mher Safaryan is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Mher received his Ph.D. in Mathematics from Yerevan State University, Armenia under the supervision of Prof. Grigori Karagulyan. Before joining KAUST, he was a Junior Researcher at the Institute of Mathematics of National Academy of Sciences, Real Analysis Department in Armenia. After that, he joined the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)

Matthias Mueller

Matthias Mueller was an Electrical and Computer Engineering Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group at Visual Computing Center (VCC) under the supervision of Prof. Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Matthias graduated with a B.Sc. in Electrical Engineering and Math Minor from Texas A&M University in 2011. After graduation, he joined P+Z Engineering in Munich, Germany as an Electrical Engineer and worked for 3 years on the development of mild-hybrid electric machines at BMW. In 2014, he started his M

Matheus Vinícius Medeiros de Souza

Ph.D. Student, Computer Science
Matheus Vinícius Medeiros de Souza is an MS/Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich. Before joining KAUST, Matheus obtained a bachelor's degree in electrical engineering from the Federal University of Rio Grande do Norte, Brazil. Research Interests Matheus's research interests include Computer Vision and Machine learning.

Matthew Sainsbury-Dale

Postdoctoral Research Fellow, Statistics
Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia, with a thesis titled "Efficient Inference for Spatial and Spatio-Temporal Statistical Models Using Basis-Function and Deep-Learning Methods", which received the Examiners Commendation for Outstanding Thesis and the Faculty of Engineering and Information Sciences Postgraduate Thesis Award. He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Matthew obtained his PhD in

Maxat Kulmanov

Research Scientist (former), Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Masheal Alghamdi

Masheal Alghamdi is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Masheal obtained her bachelor degree in Computer Science from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia in 2009. After that she joined KAUST to receive her master degree in Computer Science in 2014. Masheal is a researcher in the National Center for Computation Technology and Applied Math (CTAM) at King Abdul-Aziz City for Science and Technology (KACST)

Martins Bruvelis

Senior Information Technology Specialist, Computer, Electrical and Mathematical Sciences and Engineering
Martins is a Tier 3 IT Specialist with a background in quantum engineering and data science. He leverages analytical, multidisciplinary, and data-driven approaches to drive innovation and efficiency within the IT infrastructure. He excels at full-stack development, identifying complex problems' root causes and developing impactful long-term solutions within a broad area of competence.

Maria Sara Nour SADOUN

Visiting Student (former), Information Science Lab
Research internship in King Abdullah University of Science and Technology within EMANG as a Visiting Student on the topic of Epileptic Seizure Detection using Electroencephalography (EEG).

Majed Alzahrani

Majed Alzahrani obtained PhD degree in Computer Science from King Abdullah University of Science and Technology in 2017. His PhD thesis supervisor was Professor Xin Gao. After graduating, he joined Saudi Aramco as a data scientist in its Drilling Data Science unit. Working in one of the largest companies in the world is a dream come true for many, for Majed Alzahrani, things couldn't be better. Research Interest Majed's research interests were in machine learning, structural bioinformatics, synthetic biology.

Marco Canini

Professor, Computer Science
Professor Canini’s research seeks to improve networked-system design, implementation and operation concerning vital properties such as reliability, performance, security and energy efficiency.

Mamdouh AlJoud

Intern (former), Information Science Lab
Research Intern at King Abdullah University of Science and Technology (KAUS)

Madi Matymov

Ph.D. Student, Waves in Complex Media
Education profile MS/PhD in Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, 2022-prescent BS in Mechanics, L.N. Gumilyov Eurasian National University (ENU), 2018 - 2022 Research Interests Wave propagation, Metamaterials, Machine Learning Introduction Madi Matymov is an MS/PhD student at Waves in Complex Media (WCM) Group in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Ying Wu in her group. Before joining KAUST, Madi had studied at L.N. Gumilyov National University. Apart from that, he

Manal A. Alshehri

Ph.D. Student, Computer Science

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Lin Chen

Lin is a visiting student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST) Education and Early Career Lin is a 3rd-year bachelor student in Communication Engineering from University of Electronic Science and Technology of China (UESTC). Research Interest Lin is interested in enhancing the performance of the communication system to meet the communication requirements of the evolving new application scenarios. She’s currently exploring the application of RIS and artificial intelligence in

Lu Yu

Lu Yu obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). He mainly worked on learning representation of discrete data structure (e.g. graph, sequence, entity) that intersected with graph theory, optimization, adversarial learning. Before joining KAUST, he worked as a Data Mining Engineer at Alibaba Group. Ph.D. dissertation entitled “Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond”. First employment: Senior Algorithm Engineer, Ant Group

Lijie Hu

Ph.D. Student, Computer Science

Lizhong Ding

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Lizhong Ding worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Lizhong is a research scientist at the Inception Institute of Artificial Intelligence. Research Interests Machine learning theory Impact of approximation on learning algorithms, especially discrepancy bounds between approximate and accurate learning, for kernel learning and deep learning Large scale machine learning Large scale matrix analysis/approximation Scalable model

Lama Alssum

Ph.D. Student, Computer Science
Lama Alssum is a Ph.D. student in Computer Science at King Abdullah University of Science and Technology (KAUST) working in Professor Bernard Ghanem research group Image and Video Understanding Lab (IVUL).

Li Zhou

Ph.D. Student, Computer Science
Ms/PhD Student, CTF player, Interested in Reverse Engineering

Lama Burhan Niyazi

Ph.D. degree in the Communication Theory Lab (CTL) under the joint supervision of Professor Mohamed-Slim Alouini and Professor Tareq Al-Naffouri , King Abdullah University of Science and Technology (KAUST)

Kumail Alhamoud

M.S. Student, Computer Science
Kumail Alhamoud is a Computer Science M.S Student at KAUST working under the supervision of Professor Bernard Ghanem at the Image and Video Understanding Lab (IVUL). Before joining KAUST, Kumail obtained his B.S in Electrical and Computer Engineering at Cornell University in 2021. His current research interests include continual learning, learning from limited data under distribution shifts, and AI for Science and Biomedicine. Publications

Kuat Telegenov

Research Specialist (former), Electrical and Computer Engineering
An experienced Robotics Research Engineer and Laboratory Manager.

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Khaqan Majeed

Intern at King Abdullah University of Science and Technology (KAUST), PhD candidate at McMaster University.

Khalil Elkhalil

Postdoctoral Research Fellow, Electrical and Computer Engineering
PhD degree candidate of the Electrical Engineering, King Abdullah University of Science and Technology.

Kerven Durdymyradov

Postdoctoral Research Fellow, Computer Science
Honors and Awards Bronze Medal and Honourable Mention- International Mathematical Olympiad (IMO). Netherlands–Kazakhstan (2011, 2010) 1st and 2nd place Winner - Open Doors International Olympiad - Mathematics & AI track. Russia (2022, 2020) Silver and Bronze Medals - Balkan Mathematical Olympiad (BMO). Romania-Moldova (2011, 2010) Silver and Bronze Medals - South Eastern Mathematical Olympiad (SEEMOUS). Romania-Greece (2014, 2013)

Kamilya Smagulova

Postdoctoral Research Fellow (former), Communication and Computing Systems Lab
Education and early career Kamilya received her bachelor degree in Radio Engineering and Telecommunications from Almaty University of Power Engineering and Telecommunications (2008) and masters degree in Nanotechnology from University College London (2011). In 2021 she obtained her PhD degree in Electrical and Computer Engineering from Nazarbayev University. Currently Kamilya is a postdoctoral fellow at Communication and Computing Systems Lab (CCSL) under supervision of Prof. Ahmed Eltawil. Her research interests lie primarily in the area of machine learning and resistive hardware accelerators

Kai Yi

Ph.D. candidate working on machine learning optimization, LLMs and federated learning.

Joydeep Medhi

Visiting Student (former), Bio-Ontology Research Group
Joydeep Medhi was a visiting student from the Indian Institute of Technology, Delhi at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). During his three months internship at BORG he worked on "Machine Learning", Classification of causative disease variant(s) with mutations using Machine Learning Techniques. Successfully implemented and explored different Deep Neural Network architectures utilizing large genome sequencing data, interaction networks and ontologies and performed statistical tests for feature selection

Jürgen Schmidhuber

Professor, Computer Science

Jun Chen

Ph.D. Student, Computer Science
Jun Chen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Mohamed Elhoseiny at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jun obtained his bachelor's degree in Computer Science from Xi’an Jiaotong-Liverpool University in China, 2018. He joined KAUST as a Master student under the supervision of Prof. Robert Hoehndorf and received his master's degree in Computer Science in 2019. Research Interests Jun's research interests include machine learning, artificial intelligence, natural language processing, knowledge

Juncai He

I am currently a research scientist in Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) at King Abdullah University of Science and Technology (KAUST). I received a B.S. degree in Mathematics and Applied Mathematics from Sichuan University in 2014. In the Summer of 2019, I received my Ph.D. degree in Computational Mathematics under the supervision of Prof. Jinchao Xu and Prof. Jun Hu at Peking University in Beijing, China. From 2019 to 2020, I worked as a Postdoctoral Scholar supervised by Prof. Jinchao Xu in The Center for Computational Mathematics and Application

Jose Roberto Ayala Solares

Jose Roberto Ayala Solares was a CTL lab member and graduated from KAUST. Then, he joined the Monterrey Institute of Technology and Higher Education as a lecturer. Selected Publications M.S. Thesis: J. R. Ayala Solares, “Optimal Power Allocation of a Sensor Node under Different Rate Constraints,” King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, Jul. 2011. Conference Publications: J. R. Ayala Solares, Z. Rezki, and M. -S. Alouini, "Optimal power allocation of a sensor node under different rate constraints," in Proc. of IEEE International Conference on

José Maria Sosa Gómez

PhD student at KAUST leveraging machine learning and radar systems to enhance patient procedures, under the guidance of Prof. Mohamed-Slim Alouini at the Communication Theory Lab (CTL).

Jorge Alejandro Amador Herrera

Research Interest His research interests include scientific and visual computing, numerical simulations, machine learning, and the modelling of natural phenomena. He works on physically-based simulations. Honors and Awards During his studies at UNAM, Alejandro has been awarded many times. In 2013, he received the UNAM Scholarship from the university. He was also awarded the Telmex Foundation Scholarship from 2013 to 2016, and the CONACYT Fellowship from 2017 to 2018. He graduated with summa cum laude in 2018. Alejandro has also received multiple awards from his participation in academic

Jongho Park

Jongho Park research focuses on the design and analysis of efficient numerical methods for variational problems.

Jing Cao

Visiting Student (former), Electrical and Computer Engineering
Visiting student of MEWC 2024 in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).under the supervision of Professor Tareq AlNaffouri.

Jingtao Li

Jingtao Li was a Visiting student from University of Electronic Science and Technology of China​, at the Advanced Semiconductor Laboratory, in King Abdullah University of Science and Technology (KAUST). He is now a full-time Ph.D. student at Arizona State University (ASU). Research Interest Jingtao's research interests included machine learning, graph theory, neural network-based deep learning algorithm, and corresponding hardware architecture design. He is currently focused on reliable deep learning accelerator design, which is suitable for security-critical applications such as autonomous

Jiayao Zhang

M.S. Student, Computer Science
Jiayao Zhang is a Computer Science Master's student in the MS/Ph.D. program under the supervision of Professor Dominik Michels at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Jiayao completed her Bachelor's degree from the Chongqing University of Posts and Telecommunications. She was working with Prof. Chenqiang Gao on Event Detection and recognition topics. She was a visiting student at Shanghai Jiao Tong University (SJTU) in China with Prof. Cewu Lu in the MVIG lab and also worked as a Research

Jia-Hong Huang

Jia-Hong Huang was a Master's student in the Electrical and Computer Engineering Program under the supervision of Professor Bernard Ghanem at Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Career Jia-Hong earned a double major bachelor's degree in Statistics and Mathematical Science from National Chengchi University in Taiwan in 2014. He received his master's degree in Mechanical Engineering from National Taiwan University in 2016. After that, he joined KAUST and received his second master's degree in Electrical Engineering in 2018

Jay Morgan

Visiting Student (former), Computer Science
Jay Morgan was a visiting student at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. ​Research Interests Jay's research is focused on the verification of machine learning (ML) models, in particular, deep learning. Reasoning as to the decision process of ML is becoming increasingly important especially those that are included in safety-critical systems. Therefore, verifying the properties of the model against requirements as well as interrogating the model itself could provide a great benefit.​​ His

Jamer Jimenez Mares

Jamer Jimenez Mares is a Former visiting student from Universidad del Norte, Barranquilla, Atlantico, at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Jamer's research interests include Data analysis, Machine learning, Energy efficiency and Time-series modelling​. Selected Publications Towards an intelligent management approach for power consumption in buildings case study, “Computing Conference (CLEI), 2013 XXXIX Latin American” En, vol., no., pp.1,10, 1-5 Oct. 2012​ An analysis of

Jesper Tegnér

Professor, Bioscience
Jasper Tegnér is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.

Ismail Ben Atitallah

Ismail Ben Atitallah was a ​MS Thesis Student at King Abdullah University of Science and Technology (KAUST).

Islam Ashry

Senior Research Scientist, Photonics Laboratory
Senior Research Scientist at KAUST working in intelligent optical sensing and photonic systems, spanning sensing, communications, optical power, system integration, and AI-enabled analysis. My work has progressed from laboratory research to field deployment, patents, licensing, and commercialization. • Fellow of the Institute of Physics (FInstP) and the Institution of Engineering and Technology (FIET); Senior Member of the National Academy of Inventors (NAI) and IEEE. • Author/co-author of more than 50 journal papers and more than 40 conference papers; inventor on 14 granted U.S. patents, including six patent families licensed for commercialization. • Co-Founder of AK-Sens Limited, translating optical-sensing research into field deployments, commercial products, and industrial services. • Associate Editor of IEEE Photonics Technology Letters.

Israel Jesus Santos Filho

Israel's research is focusing on Pattern Recognition and Digital Signal Processing. He is passionate about the scientific computing ecosystem, with a particular focus on Data Science, Data Engineering, Applied and Computational Mathematics, Pattern Recognition, and Signal Processing.

Ismail AlQerm

Ismail AlQerm obtained his Ph.D. degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST). Currently, he is working as a Post-Doctorate Fellow at the University of Missouri-St. Louis, USA. Research Interests Ismail's research interests included Cognitive radios, Resource Allocation, Machine Learning, Cellular Networks, and HetNets. Thesis title: Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks. Academic Advisors Basem Shihada, Professor, Computer

Houssem Sifaou

Houssem Sifaou is a Ph.D. candidate in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Laboratory (CTL). Education and Early Career Houssem Sifaou received the Engineering Degree (Hons.) in Signal and Systems from Tunisia Polytechnic School, La Marsa, Tunisia, in 2014 and the M.S. degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2016. Scientific Interest Houssem Sifaou is interested in Random Matrix Theory

Irving Enrique Reyna Nolasco

Research Interests Irving Enrique Reyna Nolasco's research interests include numerical methods for partial differential equations, large eddy simulations, high order entropy stable schemes, machine learning, complex systems, and non-linear dynamics. Why did you choose your field of research? As a graduate student, I have always found satisfaction in developing scientific software and libraries for the simulation and visualization of different phenomena. I enjoy applying scientific computing tools and mathematical concepts to relevant problems in physics. By the end of my bachelor studies, I

Imane Boudellioua

Ph.D. (former), Computer Science
Imane Boudellioua obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Imene has a Master's degree in Computer Science from KAUST. During Master's her thesis supervisor was Professor Basem Shihada. Thesis title: " A Vehicular Guidance Wireless Sensor Actuator Network." Research Interests Imane Boudellioua's research interests include the application of machine learning and data mining algorithms for functional annotation of various biological

Igor Sokolov

Igor Sokolov is an M.S. candidate in the KAUST Peter Richtarik Research Group under the supervision of Professor Peter Richtarik. Before joining KAUST, Igor obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Igor's research interests lie in the intersection of optimization and machine learning. He is interested in both the theoretical analysis of optimization algorithms as well as their practically efficient applications.

Ibrahim Alabdulmohsin

Ph.D. Student, Computer Science
Ibrahim Alabdulmohsin obtained a Ph.D. degree in Computer Science under the co-supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory and Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Currently, Ibrahim is working with IT Strategy and Technology Planning Department at Saudi Aramco. Pursued his Ph.D. degree at MINE Lab from 2012 - 2017. Ibrahim obtained his M.Sc. degree in Computer Science from Stanford University. Dissertation Title: Learning via Query

Igor Chikalov

Senior Research Scientist, Computer Science
Research Interests Application of dynamic programming to building decision trees and decision rules Machine learning Design of algorithms and complexity analysis Selected Publications I. Chikalov, V. Lozin, I. Lozina, M. Moshkov, H.S. Nguyen, A. Skowron, B. Zielosko, Three Approaches to Data Analysis: Test Theory, Rough Sets and Logical Analysis of Data, Intelligent Systems Reference Library, Vol. 41, Springer, 2012 I. Chikalov, Average Time Complexity of Decision Trees, Intelligent Systems Reference Library, Vol. 21, Springer, 2011. I. Chikalov, P. Yao, M. Moshkov, J.C. Latombe, Learning

Hussam Wael Ismail Ibraiwish

Hussam Ibraiwish is a Master’s student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Hussam has obtained his Bachelor’s degree in Electrical Engineering from the University of Jordan in 2021. He was a visiting student at CTL in 2020 before becoming a KAUST student in 2021. Hussam was also the Vice Chair of the IEEE ComSoc in the University of Jordan. Research Interest Hussam is interested in machine learning and communication theory. He is particularly

Ibrahim Alshehri

intern at King Abdullah University of Science and Technology(KAUST)

Ibrahim Abdelaziz

Senior Research Scientist, IBM
Selected Publications Using Advanced Hidden Markov Models for online handwriting recognition. Ibrahim Hosny, Sherif Abdou, Aly Fahmy In Proceeding of Asian Conference of Pattern Recognition (ACPR), (2011) Large Vocabulary Arabic Online Handwriting Recognition. Ibrahim Hosny, Sherif Abdou, Aly Fahmy International Journal on Document Analysis and Recognition, (2014) Artificial Tutor for Arabic Handwriting Training. Sherif Abdou, Aly Fahmy, Ibrahim Hosny, Eman Mostafa In Proceedings of the Second International Conference on Arabic Language Resource, (2009) Education Profile 2013 - 2018: PhD

Humam Alwassel

Humam Alwassel was a Computer Science Ph.D. student in the Image and Video Understanding Lab (IVUL) Group under the supervision of Professor Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Humam obtained his bachelor's degree with a double major in Computer Science and Mathematics from Cornell University in New York, USA, in 2016. After that, he joined KAUST for the MS/PhD program and received his master's degree in Computer Science in 2018, and his Ph.D degree in 2023. Research Interest He’s currently focused on the development of novel

Hasan Abed Al Kader Hammoud

Hang Lu

Hajer Abbas

Visiting Student (former), Electrical and Computer Engineering
Visiting Student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Hadeel A. Ameen

Hadeel is an undergraduate student at Virginia Tech, USA. She is pursuing a computer science B.S. degree minoring in Human Computer Interaction (HCI). She joined the Secure Next Generation Resilient Systems Lab (SENTRY) led by Professor Charalambos (Harrys) Konstantinou as a Saudi Summer Internship (SSI) student. Hadeel is interested in cybersecurity and machine learning which allowed to enhance both her knowledge and experience in the uses and applications of different fields of computer science.

Hajar El Hammouti

Postdoctoral Research Fellow, Electrical and Computer Engineering
Publications dblp Scopus LinkedIn Education Profile Ph.D. in Telecommunications and Computer Science, National Institute of Posts and Telecommunications (INPT), Rabat, Morocco, Jan. 2013- May. 2017. 3rd year of engineering studies at ENSEEIHT, Toulouse, France, 2011-2012. Engineering studies at INPT, Rabat, Morocco, 2009 - 2011. Awards and Distinctions Best Paper Award, UNet 2018. Best Student Paper Award, WINCOM 2015. Best Student Paper Award, NETYS 2015.

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in

Guang An Ooi

Research Interests Guang develops systems for real-time wellbore casing inspection and 3D visualization. He constructs novel neural networks for electromagnetic inspection tools developed in conjunction with the MERGE team, which generates casing cross-sectional images from spatio-temporal data. In addition, Guang is interested in programming drilling simulators in Java and Python. Education Profile B.Sc., Aeronautics and Astronautics Engineering, National Cheng Kung University, Taiwan, 2017 M.Sc., Electrical Engineering, King Abdullah University of Science and Technology, Saudi Arabia, 2019

Guolei Sun

Guolei Sun obtained his MS degree in the Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research interests Guolei's research interests include Machine learning and data mining. Education Profile ​B.E., Biomedical Engineering, Huazhong University of Science & Technology​, 2015​. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST).

Grigory Malinovsky

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Filip Hanzely

Filip Hanzely is a recent PhD graduate from the group of Peter Richtarik. His research focuses mostly on various aspects of optimization for machine learning, designing provably efficient algorithms for solving big data problems. Filip is joining Toyota Technological Institute in Chicago (TTIC) as a research assistant professor in December 2020. Education and Early Career Filip Hanzely graduated with honor for his bachelor degree in Economics and Financial Mathematics from Comenius University in Bratislava, Slovakia. Later on in 2017, he received his master in science degree in Mathematics and

Fuad Jamour

Fuad Jamour is a Ph.D. student in InfoCloud Research Group (CLOUD) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Panagiotis Kalnis. Research Interests Fuad's research interests include Data Management and Machine Learning. Education Profile ​B.Sc. of Computer Engineering, University of Jordan, Amman, Jordan.

Francesco Orabona

Associate Professor, Computer Science
Achieving "parameter-free" machine learning is the primary focus of Professor Francesco Orabona. He is particularly interested in designing superior algorithms to train deep learning models.

Fouzi Harrou

Senior Research Scientist, Statistics
Harrou’s research focuses on statistical decision theory and its applications, multivariate statistical process monitoring, anomaly detection and diagnosis.

Fawaz Alsolami

Ph.D. Student, Computer Science
Research Interests Sequential optimization for inhibitory rules relative to length and coverage Analysis and design of classifiers based on inhibitory rules and trees Machine Learning and Data Mining Selected Publications F. Alsolami, T. Amin, M. Moshkov, B. Zielosko: Comparison of heuristics for optimization of association rules. CS&P 2015: 4-11 (2015) F. Alsolami, T. Amin, I. Chikalov, M. Moshkov, B. Zielosko: Dynamic programming approach for construction of association rule systems. CS&P 2015: 12-21 (2015) F. Alsolami, I. Chikalov, M. Moshkov: Comparison of heuristics for inhibitory rule

Fares Fourati

Fares Fourati is a Ph.D. student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini.

Fat-hy Omar Rajab

Master's Thesis Abstract The multi-agent robotic system has been proved to be more superior in undertaking functionalities, arduous or even impossible when performed by single agents. The increased efficiency in multi-agent systems is achieved by the execution of the task in a cooperative manner. But to achieve cooperation in multi-agent systems, a good localization system is an important prerequisite. Currently, most of the multi-agent systems rely on the use of the GPS to provide global positioning information which suffers great deterioration in performance in indoor applications, and also

Fangchen Hu

Visiting Student, Photonics Laboratory
Fangchen Hu was a Visiting student at Photonics Laboratory in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) at King Abdullah University of Science & Technology (KAUST). He is a Ph.D. student at Fudan University. Research Interests Fangchen's research interests included ​Advanced modulation Format, Optical communication, and Machine learning. Selected Publications ​Hu, Fangchen, et al. "Optimized Geometrically Shaped 16QAM in Underwater Visible Light Communication." 2018 IEEE Globecom Workshops (GC Wkshps). IEEE, 2018. Education Profile ​Sep 2018~Now: MS

Fabian Heilbron

Fabian Caba Heilbron was a Ph.D. student in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). He is currently a Research Scientist at Adobe Research in San Jose, California. Education and Early Career Fabian has a Bachelor’s Degree in Electronics Engineering from Universidad del Norte in Barranquilla, Colombia, 2011. In 2013, he received his Master Degree in Electronics Engineering from Universidad del Norte in Barranquilla, Colombia. He joined KAUST as a Ph.D. candidate and received his

Eva Bones

Ph.D. Student, Computer Science
Eva Bones is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Research Interest Eva is focusing on Computer Graphics and Visualization. Academic Advisors Ivan Viola, Professor, Computer Science.

Faisal Mushayt

M.S. Student, Computer Science
Education 2020.09 - Present M.S. in Computer Science, King Abdullah University of Science and Technology, KSA 2016.09 - 2020.08 B.S. in Computer Science, Boston University, USA Professional Profile 2018.06 - 2018.09 Data Scientist Intern, School of Business, LISH, Harvard, USA 2017.09 - 2018.05 Teaching Assistant, Math & Computer Science, Boston University, USA Areas of Expertise and Research Interests ​Machine Learning Learning in Physical Environments AI & Robotics Safety Engineering

Enas M. Odat

Enas Odat received the B.S. degree in computer engineering from the Jordan University of Science and Technology in 2009, and the M.S. degree from the Department of Computer Science, King Abdullah University of Science and Technology, in 2011, where she is currently pursuing the Ph.D. degree. Her research interests include machine learning, game theory, and Cyber-Physical systems. Education Profile MS. Computer Sciences, King Abdullah University of Sciences and Technology (KAUST), 2011 BS. Computer Engineering, Jordan University of Science and Technology (JUST), 2009

Eslam Abdelrahman BAKR

Ph.D. Student, Computer Science
Eslam BAKR is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny. Education and Early Career BS in Computer Engineering, Cairo University,(Sep 2012-Jul 2017)Studied programming, computer architecture, electronics, communications, waves, and computer networks. M.Sc in Computer Engineering, Cairo University,(Mar 2019-May 2022) Work Experience: TA at Zewail City of Science and Technology, (Sep 2020-21) Instructor at Faculty of Computer Science, Ain

Elnur Gasanov

Ph.D. Student, Computer Science

Elsiddig Awadelkarim Elsiddig

Postdoctoral Research Fellow, Statistics

Egor Shulgin

Ph.D. Student, Computer Science
Egor Shulgin research investigates heterogeneous, efficient, and personalized federated learning, and practical optimizers for modern deep learning.

Emaad Ahmed Manzoor

Emaad Ahmed Manzoor obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST) in 2016. Research Interests Emaad's research interests included Diffusion of information, influence in networks, Algorithms for machine learning and optimization on graphs. Education Profile MS Computer Science, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2013-2015). BE Computer Science, BITS - Pilani Goa Campus, India. (2008 - 2012) Professional

Ebrahim Al-Safadi

Visiting B.S. Student, Information Science Lab
Internship at King Abdullah University of Science and Technology (KAUST).

Dmitry Kovalev

Ph.D. Student, Computer Science
Education Dmitry obtained a B.S. degree from Moscow Institute of Physics and Technology, Moscow, Russia, in 2018. He is an M.S. in the Computer Science program at the King Abdullah University of Science and Technology as of Fall 2018. Publications Dmitry Kovalev, Samuel Horvath and Peter Richtárik. Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop, arXiv preprint arXiv:1901.08689, 2019. Dmitry Kovalev, Peter Richtárik, Eduard Gorbunov, Elnur Gasanov. Stochastic Spectral and Conjugate Descent Methods . Advances in Neural Information Processing

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.

Di Wang

Assistant Professor, Computer Science
Professor Wang's research addresses issues and societal concerns arising from machine learning, particularly in the areas of privacy, security, safety, fairness, robustness, interpretability and transparency. He aims to develop provable and practical algorithms to address trustworthiness issues in machine learning.

Dimitrios Kleftogiannis

Dimitrios Kleftogiannis obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group in King Abdullah University of Science and Technology (KAUST), in 2016. Research Interests Dimitrios works mostly on knowledge discovery technologies with applications in Bioinformatics and Computational Biology. ​His research interests include Machine Learning, Artificial Intelligence and Data Analysis of NGS-derived data. He has worked extensively on the prediction of distal DNA regulatory elements focusing on the category of enhancers. He is

David Keyes

David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

Da Li

M.S. Student, Computer Science
Da Li is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Chisato Fujii

Chisato Fujii obtained her Master's degree in Computer Science under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Chisato's research interests included Bioinformatics and Computational Biology. Born in Japan, Chisato is a software engineer at a fashion technology startup in Cambridge. She works on the development of 3-dimensional garment rendering on a customizable body shape unique to the shopper's size. How would you describe your work/activities at KAUST? ''I studied a

Cian Leo Blake

Cian is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Cian obtained BSc in Physics with a Computer Science minor from New York University Abu Dhabi (NYUAD) in 2023. Alongside his studies, Cian founded an autotech startup called Genius Autos (www.genius-autos.com) that aims to optimize the global used auto parts supply chain in the age of artificial intelligence. Research Interest Cian is focusing in the area of wireless communication

Cheng Wang

Ph.D. Student, Computer Science
Cheng Wang received his PhD in the Computer Science Program under the supervision of Professor Helmut Pottmann at the Visual Computing Center (VCC). He is now an Assistant Professor at the University of East Anglia in the United Kingdom Education and Early Career Cheng received his Bachelor's Degree in Automation from Tsinghua University, China in 2016. He joined KAUST to continue his studies and received his Master's Degree in Computer Science in 2018. Research Interest Cheng is mainly interested in Computer Graphics, Geometric Computing, and Machine Learning. Education Profile M.S., Computer

Biao Zhang

Ph.D. Student, Computer Science
Biao Zhang is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. Education and Early Career Biao Zhang obtained his bachelor degree in Mathematics and master degree in Applied Mathematics from Xi’an Jiaotong University in China. Research Interest Biao Zhang is mainly interested in machine learning and computer vision. Education Profile M.S., Applied Mathematics, Xi’an Jiaotong University, China B.S., Mathematics, Xi’an Jiaotong University

Bernard Ghanem

Professor Ghanem's research focuses on computer vision and machine learning, emphasizing large-scale video understanding, vision for automated navigation and theoretical foundations.

Azza Althagafi

Ph.D. Student (former), Computer Science
Azza Althagafi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and

Basit Olakunle Alawode

Visiting student at King Abdullah University of Science and Technology (KAUST).under the supervision of Professor Tareq AlNaffouri.

Arturo Magana Mora

​Arturo Magana-Mora obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Vladimir Bajic in 2017 and later joined the National Institute of Advanced Industrial Science and Technology (AIST) in Japan as a postdoctoral research fellow in the Com. Bio Big-Data Open Innovation Lab (CBBD-OIL). Research Interest His research interests include the development of novel machine-learning and data mining techniques to address the complex problems in biology. His research work has resulted in several peer-reviewed

Asma Abdullah Sarouji

Visiting Student (former), Electrical and Computer Engineering
Research intern at King Abdullah University of Science and Technology (KAUST) under the supervision of Prof.Tareq Al-Naffouri on health project that has a significant impact on people’s lives.

Ayan Shymyrbay

Ayan Shymyrbay is a Ph.D. candidate in the electrical and computer engineering program at the King Abdullah University of Science and Technology (KAUST).

Avetik Karagulyan

Postdoctoral Research Fellow, Optimization and Machine Learning
Avetik Karagulyan have joined the team of Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) as a Postdoctoral fellow in November 2021. Education and Early Career Avetik Karagulyan has defended his thesis at Center of Research in Economics and Statistics (CREST), Paris in June 2021 entitled as "Sampling with the Langevin Monte-Carlo" under the supervision of professor Arnak Dalalyan. In 2018, he received MSc Mathematics, Vision, Learning (MVA) diploma at ENS Paris-Saclay with highest honors. Avetik Karagulyan graduated from

Ashraf M. Kibriya

Research Interests ​My research interests include the application of Big Data and Machine Learning techniques to real-world problems in various domains, including biology and biomedicine. In particular, I'm interested in Deep Learning, Text Mining, Network Analysis and Graph Mining. Professional Profile ​2018-Present: Postdoctoral Fellow, KAUST, Thuwal, Saudi Arabia 2011-2018: Doctoral Fellow, KU Leuven, Belgium 2010-2011: Pre-doctoral fellow, KU Leuven, Belgium 2007-2009: Database developer / Datamining specialist, Datamine Ltd., New Zealand 2002-2005: Research Programmer, Machine Learning

Azimkhon Ostonov

Postdoctoral Research Fellow, Computer Science

Asaad S. Alghamdi

Arman Ibrayeva

Ph.D. Student, Mechanical Engineering
Education 2021.08 - Present Ph.D. Student, KAUST, KSA 2018.08 - 2020.07 M.S. in Mechanics, Kazakh National University, Kazakhstan 2014.08 - 2018.07 B.S. in Mechanics, Kazakh National University, Kazakhstan Professional Profile 2020.03 - 2021.11 Researcher, Institute of Mechanics and Machine Sciences, National Academy of Sciences, Kazakhstan 2019.06 - 2021.11 Engineer, KOLSAJ STROJ, Kazakhstan 2014.05 - 2015.05 Laboratory assistant, Fluid and Gas Mechanics Lab, Kazakh National University, Kazakhstan Selected Publications S. Ibrayev, N. Jamalov, A. Ibrayeva, G. Mukhambetkaliyeva "Optimal

Anna Fruehstueck

Anna Fruehstueck was a Ph. D. candidate under the supervision of Prof. Peter Wonka at King Abdullah University of Science and Technology (KAUST) and successfully defended her PhD in Computer Science on April 17, 2023. Education and Early Career Anna Fruehstueck obtained a Bachelor’s Degree in Media Informatics and Visual Computing from Vienna University of Technology in Austria in 2011. Later on in 2015, she received her M.Sc. degree in Visual Computing from Vienna University of Technology in Austria. During her studies, Anna worked as software developer at SimVis in Vienna, Austria. And in

Arnaud Dethise

Ph.D. Student, Computer Science
Arnaud Dethise is a PhD candidate in Computer Science under the guidance of Professor Marco Canini. He completed his Bachelor's degree in Engineering and Master's degree in Computer Science in 2015 and 2017 respectively, from UCLouvain, Louvain-la-Neuve, Belgium. Arnaud has a strong interest in machine learning applied to distributed systems and is currently pursuing his doctoral research in this field. His specific research interests include Neural Networks, trustworthiness, verifiability and explainability, and model privacy, with a focus on exactness and provable guarantees. Arnaud’s goals

Aritra Dutta

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Aritra Dutta is a Postdoctoral Research Fellow at the Extreme Computing Research Center (ECRC) in King Abdullah University of Science and Technology (KAUST), working with Professor Panagiotis Kalnis in his research group. Prior to that, he was a Postdoctoral Research Fellow in the Optimization and Machine Learning group of Professor Peter Richtárik. Education and Early Career Aritra Dutta received his bachelor degree in Mathematics (Hons.), Minor in Physics and Statistics from Presidency College in Calcutta University. He obtained a master degree in Mathematics and Computing from Indian

Amine Bejaoui

Amine Bejaoui was an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amine Bejaoui obtained his engineering degree with majors in Economics and Scientific management from Ecole Polytechnique de Tunisie (EPT) , Tunisia, in 2015. Research Interest The scientific interests of Amine Bejaoui lies in the area of statistics, machine learning and the applications of Random Matrix Theory. Education Profile M.Sc. in Statistics, KAUST, Aug 2018 – Present
kaust-cemse-ece-amin-almozel

Amin Almozel

Amanat Kafizov

Amanat Kafizov is a Ph.D. student in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), specializing in visible light Terahertz (THz) communications and machine learning.

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria

Alyazeed Basyoni

M.S. Student, Computer Science
Alyazeed Basyoni is a CS Master Student under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Alyazeed joined Carnegie Mellon University in Pittsburgh, Pennsylvania and earned his bachelor degree in Computer Science with minor in Mathematics in 2018. Education Profile B.S., Computer Science, Carnegie Mellon University, USA, 2018

Alya AlShaikh

Alya AlShaikh is currently an undergraduate student at Alfaisal University in Riyadh, KSA. She is pursuing a Software Engineering BSc specializing in Cyber Security and Machine Learning. Alya joined the Secure Next Generation Resilient Systems Lab (SENTRY) led by Professor Konstantinou as an Accelerated Bachelor’s and Master's (ABM) student. Her research interests include applying machine learning techniques to enhance cyber defense strategies.

Alexandra Irger

M.S. Student, Computer Science
Alexandra Irger is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Before coming to KAUST, Alexandra graduated from TU Wien with a BSc in Media Informatics and Visual Computing in 2023. Research Interest Alexandra is focusing on Scientific Visualization and Computer Graphics.

Alam Zaib

Research Intern at King Abdullah University of Science and Technology (KAUST) ,Assistant Professor / COMSATS institute of information technology, Abbottabad.

Aku-Jaakko Alexis Kammonen

Aku-Jaakko Alexis Kammonen is a Research Scientist at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST).

Ahmed Foud Ali Mehdi AlMehdhar

Visiting Student (former), Electrical and Computer Engineering
Visiting Student at King Abdullah University of Science and Technology (KAUST).

Ahmed Mohamed Abdelmoniem Sayed

Dr. Ahmed Sayed is a Postdoctoral research fellow at ECRC, working with Professor Marco Canini. He is focused on tackling challenging problems related mainly to improve the performance and understanding of machine learning systems and their usage for optimizing and automating computer and network systems. Why KAUST? KAUST is the beacon for innovation and creativity in the middle-east area and they follow the model of HKUST to become a world-wide top-tier leading university in science and technology. Why networking The intersection point between Networked Systems & Machine Learning is a very

Ahmed Abdulmajeed Alabdulkarim

Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Ahmed Douik

Visiting B.S. Student, Computer, Electrical and Mathematical Sciences and Engineering , Quantitative Researcher at Chicago Trading Company PhD candidate at California Institute of Technology

Abdulwahab Felemban

Ph.D. Student, Electrical and Computer Engineering CEMSE, King Abdullah University of Science and Technology (KAUST),completed MSc under the supervision of Prof. Tareq Al-Naffouri.

Adil Salim

Postdoctoral Research Fellow, Computer Science
Adil Salim is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Salim earned his Master degrees in Data Science and Statistical Engineering from ENSAE ParisTech and in Probability Theory and Stochastic Modeling from Paris XI University in France in 2015. He received his Ph.D. in Machine Learning from Télécom ParisTech in France in 2018. Before joining KAUST, Adil worked in many positions starting from an Auditor for banks and insurance companies

Abeer Almutari

M.S. Student (former), Computer Science
Abeer Almutari obtained her Master's degree in Computer Science under the supervision of Professor Robert Hoehndorf at Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Abeer's research interests included bioinformatics, machine learning and building new automated models using artificial intelligence. Education ​B.Sc. Electrical & Computer Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia, 2016

Abdulrahman Alanazi

Visiting Scholar (former), Information Science Lab
Visiting Scholar, Information Science Lab at Computer, Electrical and Mathematical Science and Engineering (CEMSE) , Assistant Professor at King Saud University

Adel Bibi

Adel Bibi is a postdoctoral research assistant in computer vision and machine learning at the Torr Vision Group working with Professor Philip Torr at the University of Oxford. Prior to that, he received his MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) where he was part of the Image and Video Understanding Lab (IVUL) advised by Professor Bernard Ghanem. Education and Early Career Adel Bibi has a Bachelor’s Degree in Electrical Engineering from Kuwait University in 2014. He received his Master Degree in Electrical Engineering from King Abdullah University of

Abdulmajeed Aljaloud

Visiting Student, Computer Science
Visiting Student Research Program ,Research Intern at King Abdullah University of Science and Technology (KAUST)

Abdulellah Abualshour

Abdulellah Abualshour is a Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdulellah was chosen to join the KAUST Gifted Student Program (KGSP). He is a graduate of Rutgers University where he received a B.S. degree in Computer Science, with a focus on data science and artificial intelligence. Regarding his early career, Abdulellah did many internships during his studies. In 2015, he worked for 1 year as a Public Relations Officer at Rutgers U niversity in

Abla Kammoun

Senior Research Scientist, Electrical and Computer Engineering
Abla Kammoun focuses on the development of random matrix theory tools in view of their applications to wireless communication, signal processing and machine learning.

Abdullah Al-Nafisah

Master student at the Information Science Lab (ISL), in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Abdullah Khamis

Abdullah Khamis graduated with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao. He now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia. He leads the Data Science team in developing several machine learning based predictive models such as sentiment analysis and prediction. Research Interest Abdullah's research interests were in bioinformatics and machine learning.

Abdullah Muhammad Kamal

Visiting Student (former), Computer Science
Abdullah Muhammad-Kamal is a visiting student from the University of Warwick, United Kingdom at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Currently on his placement year, interning with the Bio-Ontology Research Group at KAUST, he will be graduating with his Bachelor's degree in Computer and Management Sciences in the summer of 2021. Research Interests ​Abdullah's research interests include Bioinformatics, Machine Learning, and Data Science.

Abdulrahman Aljedaibi

Electrical Engineering PhD student at CEMSE, King Abdullah University of Science and Technology (KAUST).

Abdullah Ahmed Zayat

Visiting Student Research Program under the supervision of Professor Tareq Al-Naffouri ,Research Intern at King Abdullah University of Science and Technology (KAUST)

Abdullah Adil Albakry

Senior Associate Consultant, Bain & Company
Education and early career: I graduated with my bachelor’s degree in Electrical and Computer engineering with a mathematics minor from North Carolina State University (NCSU) in the United States. Now, I am a MS/PhD student at KAUST supervised by Dr. Ahmed Eltawil. Research Interest: Machine Learning and Communication Education Profile B.Sc., Electrical and Computer Engineering, North Carolina State University, United States, 2020.

Abdullah Hamdi

Abdullah Hamdi was a Ph.D. student in the Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career In 2016, Abdullah Hamdi obtained his bachelor's degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran, Saudi Arabia. After that, he joined KAUST to pursue the same field of study and received his master's degree in 2017 in the Computer Vision track. Research Interest Machine Learning Robustness of Deep Learning Computer

Abderrazak Chahid

Abderrazak Chahid is a PhD candidate in Electrical and Computer Engineering department within CEMSE division. He obtained his Bachelor of Science degree in Electrical Engineering and Power Electronics from Sultan Moulay Slimane University in Morocco. He then got a Master degree in Electrical Engineering from the National School of Applied Sciences in Morocco before obtaining a second Master of Science degree in Embedded Systems from Lorraine University in France. Abderrazak is currently working on developing biomedical signal/image processing: MRS water suppression, MRI image denoising. He is

Abdallah Al-Moubayed

MS Degree , Computer, Electrical and Mathematical Sciences and Engineering ,King Abdullah University of Science and Technology (KAUST) .Now Abdallah is Visiting Student to (KAUST)
<> artificial intelligence (234)

Saudi HPC/AI Conference 2026

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KAUST

Join researchers, technology experts and industry leaders to explore how HPC and AI are advancing research and innovation.

FOCUS cuts wasted computing in diffusion language models

2 min read ·
KAUST-led research identifies how diffusion language models can avoid unnecessary calculations, increasing throughput by up to 3.52 times while maintaining generation quality.

Understanding Practical Optimization in Machine Learning

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This dissertation studies how practical optimization methods used to train modern machine learning systems can be better understood by analyzing the objectives, updates, assumptions, and approximations that arise in their actual implementation.

Yousef Radwan

M.S. Student, Computer Science

Private and Robust Learning for Decision Making

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This thesis develops the theoretical foundations of decision-making under privacy, heavy-tailed feedback, and data contamination, establishing fundamental limits and designing near-optimal algorithms across bandits, reinforcement learning, and RLHF.

The Role of Humans in Scientific Discovery in the Age of LLMs — Beyond Asking: Turning LLMs into Research Collaborators

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B9 R2325

Rather than offering definitive conclusions, this talk seeks to stimulate dialogue, question assumptions, and inspire new forms of collective thinking about the future of scientific research and doctoral training in an AI-driven world.

Bushra Bin Hemid

M.S. Student
MSc/PhD candidate in Artificial Intelligence at KAUST. Expertise in AI and information security. Research focuses on AI for infrared imaging, transforming sensing technologies for healthcare, agriculture, and smart infrastructure

Synergizing Nanophotonics and Artificial Intelligence: From Inverse Design to Intelligent Optical Frontends

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B3 R5220

This dissertation investigates the transformative convergence of nanophotonics and artificial intelligence (AI).

Yanda Meng

Assistant Professor, Bioengineering
Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

KAUST becomes first FIFA research institute in the Middle East and Asia

1 min read ·
KAUST has been designated the first FIFA Research Institute in the Middle East and Asia, and the fifth worldwide, reflecting the University’s commitment to advancing innovation in football through cutting-edge scientific inquiry.

KAUST, in collaboration with MCIT, to host the second edition of the MENA Machine Learning Winter School (MenaML)

1 min read ·
A week of advanced instruction and exchange for emerging machine learning talent.

Data-driven Anomaly Detection in Industrial Processes

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B9 L2 R2325

This talk presents a model-based anomaly detection framework, along with data-driven process monitoring approaches based on multivariate statistical methods and artificial intelligence techniques.

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Rising Stars in AI Symposium 2026

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KAUST Campus

Join the AI research community for a multi-day symposium focused on emerging research directions and collaboration.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent

Nuclear Fusion Powered by AI and HPC

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B9 L2 R2325

This talk presents advanced simulations with AI-driven optimization to improve the performance of a next-generation plasma-jet-driven magneto-inertial fusion concept.

Artificial intelligence takes to the skies to protect a Saudi tradition

1 min read ·
KAUST scientists develop an affordable and simple AI system for local camel herders

Deloitte and KAUST join hands to explore AI applications in Saudi Arabia

2 min read ·
Partnership will focus on research, education, and real-world AI solutions to help advance Vision 2030
Salem Alsaqaf

Salem Al-saqaf

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.

Visual Analytics for Macromolecular Science

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This dissertation presents a suite of scientific visual analytics systems that tightly couple large-scale computational modeling - including both AI-powered inference and traditional molecular simulations - with interactive, scalable visualization.

Bridging AI and Spatial Biology to Navigate Biological Landscapes

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This thesis presents several studies by proposing AI-driven methods to supply a comprehensive and precise representation of biological landscapes via spatially resolved transcriptomics (SRT) data.

SAAI for Kids hackathon introduces Saudi youth to creative uses of AI

3 min read ·
More than 200 students joined KAUST’s SAAI Hackathon to explore AI in art, music and design

A blindfold approach improves machine learning privacy

1 min read ·
A query-based method for extracting knowledge from sensitive datasets without showing any underlying private data could resolve long-standing privacy concerns in machine learning.

AI for Chips and Chips for AI

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B9 L2 R2325

This seminar presents a unified "AI for Chips & Chips for AI" approach, demonstrating how AI enhances semiconductor design while specialized silicon accelerates AI computation, creating a rapid innovation cycle.

Towards Trustworthy News Recommendation Systems

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B3 L5 R5209

This thesis enhances the trustworthiness of news recommendation systems by addressing the cold start problem for new users/items, enabling efficient user data removal for privacy, and mitigating multi-dimensional filter bubbles to improve diversity.

Design and Process Development of Graphene-Based Geometric Diodes for Enhanced Performance

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B2 L5 R5220

This thesis advances graphene geometric diodes (GGDs) by addressing fabrication, performance, and design challenges using innovative nanofabrication techniques and artificial intelligence-enhanced computational methodologies.

Making LLMs Agentic: How Tool Use and Synthetic Data Drive Real-World Intelligence

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B2/3 L0 R0215

This talk presents advancements in agentic AI, focusing on creating and utilizing datasets and models that enable Large Language Models to effectively interact with external tools for real-world applications.

SAAI Factory 3

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Buildings 20 and Buildings 19

SAAI Factory 3 brings together artists, researchers and youth to explore AI and art at KAUST.

Professor Wolfgang Heidrich elected as a Fellow of the National Academy of Inventors and named a 2025 Optica Fellow

5 min read ·
KAUST Professor Wolfgang Heidrich has been named a Fellow of both the National Academy of Inventors (NAI) and Optica, recognizing his pioneering contributions to computational imaging, display systems and AI-driven optical technologies.

Najmeddine Dhieb

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Yasmeen Alsaedy

M.S. Student, Computer Science
She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)
COEfoodsecurity

AI-enabled UAVs and Multimodal Monitoring for Protected and Controlled Environment (PACE)

This project aims to utilize advanced technologies for UAVs to enhance the efficiency, accuracy, and responsiveness of crop monitoring and management within the diverse PACE settings. The primary objectives revolve around employing a fleet of micro-UAVs equipped with cameras and LiDAR sensors to capture high-resolution images and 3D point clouds and perform multi-layered AI analysis. While cameras offer visual insights into surface-level plant health and characteristics, LiDAR provides deeper insights about plant physical attributes for a more comprehensive crop assessment. This project is

Circuits and Systems for Efficient Machine Learning and Artificial Intelligence

Machine Learning (ML) and Artificial Intelligence (AI) applications require the use of more and more advanced algorithms for extracting meaningful information from increasingly (and sometimes incredibly) large sets of data. While the algorithmic part has recently seen significant advances (as for instance through the adoption of Deep or Convolutional Neural Networks), it sometimes comes at the cost of high computational complexity which hinders their straightforward implementability. This activity aims at advancing in this direction by: proposing architectures to reduce the computational cost

On-chip lasers for optical computing

Optical computing photonic hardware accelarators aims to create faster and more efficient computing systems and artificial intelligence systems. Optical computing utilizes light instead of electricity to transmit data, which can potentially lead to significant increases in speed and power efficiency. On-chip lasers play a crucial role in this field by providing the necessary light sources on the same single chip. The development of on-chip lasers requires a multidisciplinary approach, combining expertise in materials science, computer science, and photonics. Despite the challenges involved

AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

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B2 R5220

The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high ​throughput sequencing technologies in the life sciences.

Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

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B2 L5 R5220

Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a

Causality-based new drug development: Some successful cases and a new challenge

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B2 L5 R5209

Abstract The success rate of new drug development is extremely low. It is even lower when the new drug has a novel mechanism of action. From my experience, I propose that the success rate can be dramatically increased by predicting the effects of a drug in humans based on the causality-confirmed data. It is dangerous to develop new drugs based on the data in which causality is not confirmed. In biology, there are three different types of relationships in which the causality is confirmed, i.e. the relationships between parent and child, between gene and phenotype and between intervention and

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Paving the Way for Next Generation Wireless Data Center Networks

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B1 L4 R4214

As big data, articial intelligence, cloud services, cellular infrastructure, content delivery; all of which entail interconnected and sophisticated computing and storage resources. Recent studies on traditional data center networks (DCNs) revealed two key challenges: a biased distribution of inter-rack trac, and unidentied ow multi-classes best known as delay sensitive mice ow (MF) and throughput-hungry elephant ow (EF).

Efficient Localization of Human Actions and Moments in Videos

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B3 L5 R5220

Abstract We are stumbling across a video tsunami flooding our communication channels. The ubiquity of digital cameras and social networks has increased the amount of visual media content generated and shared by people, in particular videos. Cisco reports that 82% of the internet traffic would be in the form of videos by 2022. The computer vision community has embraced this challenge by offering the first building blocks to translate the visual data in segmented video clips into semantic tags. However, users usually require to go beyond tagging at the video level. For example, someone may want

Knowledge-based methods in biomedicine

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B9 L2 H1

The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to apply knowledge-based methods for the analysis of biological and biomedical data, in particular identification of gene-disease associations and drug targets.

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

KAUST Research Conference Digital Health 2020

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B19 L2 H1

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Amazon Web Services (AWS) ML Immersion Day

Mohib Khan, Hesham Abouelmagd, Shijaz Abdulla (AWS)

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B19 H1

The ML Hub with the support of the AI Initiative, is excited to be hosting the AWS ML Immersion Day! Join us for a full-day immersion tutorial and hands-on lab on Amazon’s ML tools. The program includes an introduction to AWS AI and machine learning services and hands-on module on Amazon Lex and SageMaker.

Machine Learning Scan of Complete Genome Sequences of 4,500 Rice Varieties reveals Secrets in the Domestication Process of Crops

Takashi Gojobori, Distinguished Professor, Bioscience

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B9 L2 H2 R2325

In the history of humankind, domestication was invented as anthropogenic evolution that fulfills mankind’s critical food demand. The domestication is simply based on mating processes and subsequent selection processes to pick up better hybrid offspring that have advantageous combinations of genomes. Taking advantage of machine learning classifier, we discovered a number of sub-genomic regions that have been incorporated in the rice genomes through production of hybrid offspring during domestication. This so-called “introgression” event is disclosed as an essential key of domestication process. This eventually leads to construction of the AI-aided Smart Breeding Platform to accumulate all the breeding histories of crop species into an Integrated Breeding Knowledgebase.

KAUST-MBSC Invitational Healthcare Analytics and Data Science Workshop

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B3 L5 R5220

The amount of collected medical data and computational power are growing rapidly, which creates new opportunities to impact and improve healthcare by advanced analytics and data science. We aim to bring together practitioners and researchers from across the Kingdom to exchange knowledge and explore possible collaborations. To register, please, fill form here.

SIAM CSE21 (VIRTUAL) - Minisymposium on HPC Response to the COVID19 Pandemic

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KAUST

Abstract This minisymposium brings together experts in numerical simulation that have developed HPC software tools toward a better understanding coronavirus and the global COVID19 pandemic. Their recent results highlight the importance of computational science as a guiding tool that helps in uncovering the structure and mechanics of the viral spread as well as drastically reduces the number of candidate treatments that need laborious laboratory testing. From transformative medical advances to drug discovery driven by massively parallel supercomputing, this minisymposium will span a range of

The Robots are Coming – to your Farm

Girish Chowdhary, Associate Professor, UIUC

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KAUST

Featured Sessions
For more information please visit the website

KAUST Smart Health Initiative: Importance of AI and ML

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KAUST

Abstract Data collection, storage and management has been always the cornerstone of any successful and efficient healthcare system. The recent rapid and impressive advances in data collection tools such as artificial intelligence (AI) technology, and its deep-learning subtype in particular, provide a unique opportunity of using data in a more efficient and cost-effective ways. Indeed, AI and ML (Machine Learning)-based management of health data is revolutionizing our approaches to disease treatment and data handling at every level that led to, among other things, the birth of personalized and

Physics-based Modeling Meets Machine Learning

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KAUST

Abstract The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in scientific and visual computing. This talk covers a selection of previous and current work presenting various synergy effects between physics-based modeling and machine learning ranging from the generation and the enhancement of synthetic data to data-driven approaches boosting the performance of physics simulations. Brief Biography Since summer 2016, Prof. Michels is heading the Computational Sciences Group

AI for Cybersecurity vs. Cybersecure AI: a chicken and egg problem?

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KAUST

Abstract Artificial Intelligence (AI) is a top subject in the expectations about emerging problem-solving technologies in a number of sectors, given advances in their practicality. Cybersecurity is one such target, and like any hype subject appearing in the homonym Gartner curve, there is a chance that expectations are somewhat inflated. In this talk, I will start by briefly reviewing how AI/ML have been showing promise in a number of security and dependability areas. Then I will play devil’s advocate, challenging the audience to think whether AI/ML mechanisms can help cybersecurity, without

Enabling Safe Cyber-Physical System autonomy with AI

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KAUST

Abstract With the advent of increasingly intelligent algorithms, robots and other cyber-physical systems are capable of planning and performing increasingly challenging and creative tasks. Safety, however, remains an essential requirement on many robotic behaviors. It is also a property that is hard or impossible to prove for virtually all intelligent algorithms of practical value. Ariadne is a constructive, model-based paradigm that enables the safe operation of many robotic systems, even though the algorithms involved with the operation may not be verifiable. Ariadne, or "plan B" engineering

Practical Lessons and Opportunities from Testing Autonomous Mobility at KAUST

Mohamed Abdel-Aal

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KAUST

Abstract KAUST is a living lab for future mobility and transportation innovations such as autonomous driving. The roads, the progressive community, and the traffic regulations make KAUST a great place to examine and assess various mobility options in collaboration with industry partners, government agencies, and startups. KAUST launched the first autonomous shuttles pilot in Saudi Arabia in December 2019. The pilot is a platform to develop unique smart city experiences that enable the scientific community, the residents of KAUST, our partners, and entrepreneurs to create an innovative

Fish Behavior Recognition and Data-driven Control for Smart Aquaculture

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KAUST

Abstract In this talk, I will present our progress in controlling fish feeding and monitoring water quality in smart aquaculture systems. The idea consists of combining computer vision tools along with optimal control strategies. I will first show some recent results on implementing reinforcement learning for optimal fish feeding and temperature monitoring. Then, I will discuss the implementation of computer vision tools as an essential component for analyzing fish behavior. I will conclude with some algorithms for the synthesis of computer vision-based classifiers that enable control

Automation and Insights for Improved Oilfield Operations

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KAUST

Abstract The challenges experienced by the oil and gas sector over the past decade have been a driver for change and technology adoption. In addition to an overview of oilfield applications that continue to experience the benefits of today’s digital transformation, the talk will shed some light on oilfield research and technology developments at KAUST. Brief Biography Shehab Ahmed is Professor of Electrical & Computer Engineering. He holds a joint appointment as Professor of Energy Resources and Petroleum Engineering at KAUST and is a member of the Ali I. Al-Naimi Petroleum Engineering

The AI Initiative at KAUST

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KAUST

Brief Biography Bernard Ghanem is currently an Associate Professor in the CEMSE division, a theme leader at the Visual Computing Center (VCC), and the Interim Lead of the AI Initiative at KAUST. His research interests lie in computer vision and machine learning with emphasis on topics in video understanding, 3D recognition, and theoretical foundations of deep learning. He received his Bachelor’s degree from the American University of Beirut (AUB) in 2005 and his MS/PhD from the University of Illinois at Urbana-Champaign (UIUC) in 2010. His work has received several awards and honors, including

Counterfactual Representation Learning with Balancing Weights

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KAUST

Abstract A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. In this work, we discuss the pitfalls of these strategies - such as a steep trade-off between achieving balance and predictive power - and present a remedy via the integration of balancing weights in causal learning. Specifically, we theoretically link balance to the quality of propensity estimation, emphasize the importance of identifying a proper target population, and elaborate

Recent Advances in Optimization for Machine Learning

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KAUST

Abstract In this talk I will briefly and at a high level outline several recent (obtained in the last 2 years) research results obtained in the Optimization and Machine Learning lab at KAUST. In general, we work on the development of fundamental optimization algorithms for machine learning, including randomized, parallel, distributed, decentralized and federated algorithms, and the associated mathematical theory. Our goal is typically the development of new methods that would have state-of-the-art theoretical properties, with the explicit aim for these methods to be also practical. I may touch

From Private Learning to Trustworthy Learning

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KAUST

Abstract Recent research showed that most of the existing machine learning algorithms are vulnerable to various privacy attacks. An effective way for defending these attacks is to enforce differential privacy during the learning process. As a rigorous scheme for privacy preserving, Differential Privacy has now become a standard for private data analysis. In this talk, I will present some recent developments in trustworthy machine learning from a differential privacy view. In the first part, I will talk about some pitfalls and trustworthy issues (such as resilience and fairness) in the current

Training Machines to Recognize Reliable Protein-Protein Docking Pose

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KAUST

Abstract Protein-protein interactions drive many important biological events such as infection, replication, and recognition. To control or engineering such events, we need to access the molecular details of the interaction provided by experimental 3D structures. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions. Computational modeling like protein-protein docking can help to fill this gap by generating docking poses. Protein-protein docking generally consists of two parts, sampling, and

Recent Topics in Generative Modeling

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KAUST

Abstract In this talk, I will talk about recent topics in generative modeling. I will discuss some interesting research directions in the research community in general and also mention some works of my own group. Brief Biography Peter Wonka is Full Professor in Computer Science at King Abdullah University of Science and Technology (KAUST) and Associate Director of the Visual Computing Center (VCC). Peter Wonka received his doctorate from the Technical University of Vienna in computer science. Additionally, he received a Masters of Science in Urban Planning from the same institution. After his

Accelerated Deep Learning via Efficient, Compressed and Managed Communication

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KAUST

Abstract Scaling deep learning to a large cluster of workers is challenging due to high communication overheads that data-parallelism entails. This talk describes our efforts to rein in distributed deep learning's communication bottlenecks. We describe SwitchML, the state-of-the-art in-network aggregation system for collective communication using programmable network switches. We introduce OmniReduce, an efficient streaming aggregation system that exploits sparsity to maximize effective bandwidth use. We touch on our work to develop compressed gradient communication algorithms that perform

Attackability of Machine Learning Models with Applications in the Discovery of Safety-critical Aerial Operations

Prof. Xiangliang Zhang

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KAUST

Abstract In safety-critical application domains, it is crucial to assess the attackability of the employed machine learning models and to design fault-tolerant models against the noise and attacks. This talk will first introduce our study on characterizing the attackability of a targeted classifier on categorical sequences and a targeted multi-label classifier under evasion attack. A fault-tolerant model is then presented to handle the noisy data existing in the machine learning input. Our application aim is to find safety-critical aerial operations. We will introduce the development of a deep

AI and Health – Challenges and Opportunities

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KAUST

Abstract There is a fair amount of hope, if not hype, to the extent that computational techniques, including AI, can transform our understanding of biology, early discovery of diseases, and augment health. From some real-world examples from our work, I'll discuss challenges and opportunities in this space. Brief Biography Professor at KAUST (08/2016), affiliated with Bioscience, Computer Science, Statistics, and Bioengineering programs. He had been acknowledged with honors for best teaching; physiology & neuroscience to medical students in Medical School (Karolinska Institute) and machine

AI-enabled Analysis of Our Social Expression and Interactions in Digital Age

Prof. Xiangliang Zhang

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KAUST

Abstract Half of the total global population are using social media and produce a huge amount of user-generated content and interaction records on Facebook, Twitter, Instagram, YouTube, LinkedIn and so on. AI and machine learning techniques have been leveraged to understand the unprompted feelings, opinions and preference of users by analyzing their social expression and interactions. This talk will introduce the advantages brought by the AI-powered recommendation systems, from which we have benefited to have an easy discovery of the posts we like to see on our favorite social networks, the

Revealing functional and phenotypic consequences of molecular aberrations through ontologies

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KAUST

Abstract An ontology is an explicit specification of a conceptualization of a domain. Biomedical ontologies are a foundation of bioinformatics infrastructure and are used both to integrate data and to formally describe biological phenomena. I will show how to use ontologies to reveal consequences of molecular changes and aberration. Brief Biography Robert Hoehndorf is an Associate Professor in Computer Science at KAUST. His research focuses on the development and application of knowledge-based methods in biology and biomedicine.

Artificial Intelligence(AI) Inspired Designs of Acoustic and Photonics Systems

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KAUST

Abstract In this talk, I will introduce our recent progress on how deep learning models inspired us in solving real world problems with different applications. In particular, I will focus on deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak, which can conceal an object from incident acoustic waves over a broad frequency range; and a deep learning framework based on fully connected neural network to design plasmonic metascreen for efficient light trapping in ultrathin silicon solar cells. Brief Biography Dr. Ying Wu is an associate professor in

On Solving a Key Challenge in Federated Learning: Local Steps, Compression and Personalization

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KAUST

Abstract We propose a new optimization formulation for training federated learning models. The standard formulation has the form of an empirical risk minimization problem constructed to find a single global model trained from the private data stored across all participating devices. In contrast, our formulation seeks an explicit trade-off between this traditional global model and the local models, which can be learned by each device from its own private data without any communication. Further, we develop several efficient variants of SGD (with and without partial participation and with and

RNA secondary structure prediction – revisiting an old problem from new perspectives

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KAUST

Abstract RNAs need to form specific secondary structures for their functions. RNA secondary structure prediction is among the early problems being tackled in bioinformatics, yet far from being solved. The dominant dynamic programming-based methods cannot handle pseudoknots, which are present in about 40% of RNAs, whereas heuristic methods and recent deep learning methods still do not have satisfactory performance. In this talk, I will introduce our two recent works, NNfold and E2Efold, both of which are deep learning-based methods for RNA secondary structure prediction. NNfold predicts the

Machine Learning for Science: Learning Representations and Governing Equations from Scientific Data

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KAUST

Abstract In essence, science is about discovering regularities in Nature. It turns out that such regularities (laws) are written in the language of mathematics. For example, fundamental equations such as Maxwells, Newtons, mean-field, and renormalization techniques in statistical mechanics enable deep understanding, often in combination with computational modeling and simulations. In many cases, laws can be formulated and refined from fundamental "first principles" such as the variational principle of least action in mechanics. Yet, in phenomenological areas, including biology, we have data

Towards AI-based precision medicine in breast cancer – from molecular-, to imaging-, to radiogenomics analysis

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KAUST

Abstract Breast cancer is the leading cause of cancer-related death in women worldwide. A promising strategy to improve breast cancer survival rates is precision medicine--a targeted approach to the diagnosis and treatment that are tailored for individual patients considering their genetic, environmental, and lifestyle backgrounds. In this talk I will introduce our series of work towards precision medicine in breast cancer. I will first describe, at a pan-cancer level, how we used as few as 58 genomic features to build accurate predictors to detect cancer patients from normal people, to

Imagination supervised Machines that can See, Create, Drive, and Feel

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KAUST

Abstract Most existing learning algorithms can be categorized into supervised, semi-supervised, and unsupervised methods. Most of these approaches rely on defining empirical risks or losses on the provided labeled and/or unlabeled data. In this talk, we will reflect on a class of methods that goes beyond labeled/unlabeled training data, improving task performance by either raw or latent imaginary data. We refer to this class of techniques as imagination supervised learning, and we will dive into how we developed several approaches to build machine learning methods that can See, Create, Drive

Intelligent Design and Discovery of Molecules

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KAUST

Abstract Quantitative structure-property relationships (QSPRs) using machine learning tools to relate molecular structural features of pure hydrocarbons to a range of physical and chemical properties is of interest. Mathematical modelling of these relationships can be very complex, which makes the deep learning networks (DNNs) ideal candidates for the QSPR development. Here we show QSPR development for a chemical property related to the sooting tendency of a fuel, which is directly linked to high and undesirable particulate matter emissions to the atmosphere, and therefore is recognized as a

Next-Gen Mapping and Monitoring of Coral Reefs with SLAM and SfM

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KAUST

Abstract The Red Sea Development Company (TRSDC), one of Saudi’s premiere GigaProjects, aims to develop a world-class, hyper-luxury resort complex in the Red Sea. The project is located between the coastal cities of Umm Lujj and Al- Wajh, encompassing one of the Red Sea’s most unique geological and biological habitats, the Al-Wajh lagoon. This location includes the Red Sea’s largest barrier reef and the Red Sea’s largest lagoonal habitat (1500km2), within which can be found nearly 100 islands and many species not known from any other part of the world. As a result of TRSDC’s commitment to

IMCP: An Efficient In-Memory Convolutional Neural Network (CNN) Processor

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KAUST

Abstract Even though CNNs achieve great success in performing complex intelligence tasks, this achievement comes with an overwhelming cost. Modern CNN architectures require hundreds of stacked convolution layers, performing billions of operations for a single input. To meet the unique needs of AI-based systems, various architectures were proposed as possible contenders such as GP-GPUs or application-specific processors like Google TPU and Intel Movidius. These architectures focus on utilizing massive parallelism to address the computational needs; however, the issue of memory access is still

Artificial Intelligence methods for precision health: Applications to rare disease and cancer research

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B9 L2 H2

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will show how to incorporate biological background knowledge in machine learning models for the identification of gene-disease

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

CBRC Dual Seminar - Analysis of COVID-19 Severity - DeepSVP computational method

Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student

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KAUST

Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.

Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

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B9 L2 R2322 H1

This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

Applied AI for sustainable development

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B9 L2 R2322 H1

Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.

A journey in applied complexity: pioneering record-performing technologies in clean energy harvesting, machine vision, and smart materials - Public Colloquium

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B9 L2 R2322

Applied Complexity aims to understand the physical origin of these behaviors and transform them into sustainable technologies that tackle global problems of global interest. These range from energy harvesting to clean water production, the design of smart materials, biomedical applications, information security, artificial intelligence, and global warming. In this talk, I will summarize my group's recent research, discussing present results and future challenges of Applied complexity both as a science and engineering.

ECRC Activities at SC22!

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Dallas TX USA

HPC accelerates! This year's motto of the Supercomputing conference...

Machine learning in hardware via trained metasurface encoders: theory, design and applications

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B2 L5 R5209

In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

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B9 L2 R2322 H1

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

The Wild West of NLP Modeling, Evaluation, and Documentation

Dr. Nazneen Rajani, Research Lead, Hugging Face, California

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B2 L5 R5220

Are commercial APIs from Microsoft, Google, and Amazon for NLP tasks any better than rule-based systems? Is documentation for LLMs accessible to non-expert users in the industry? Our work on creating a unified toolkit for evaluation (robustness gym) and reporting (interactive model cards) attempts to address these questions.

Machine-learning-assisted Quality of Transmission Estimation in Optical Networks

Andrea Bianco, Full Professor, Electronics and Telecommunications Department at Politecnico di Torino

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B9 L4 R4125

Machine Learning (ML) tools have recently been adopted for a wide range of automated operations in optical networking, moving fundamental steps towards the paradigm of zero-touch infrastructures. One example of such tasks is estimating the Quality of transmission of a lightpath prior to its establishment, which is particularly challenging due to the non-linear characteristics of signal propagation in optical fibers and to the often-incomplete knowledge of equipment parameters. This talk provides an overview of the contribution of my research team in the field of ML-based lightpath QoT estimation, including transfer learning approaches for inter-domain model adaptation, active learning for model building with small-sized training dataset, quantification of prediction uncertainty, and adoption of Explainable AI framework to expose the internal decisional mechanisms of trained models.

The 2nd SAAI Factory Hackathon Kickoff Symposium at KAUST

The 2nd SAAI Factory Hackathon Kickoff Symposium 2023

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B20 Auditorium

We are pleased to invite you to the second SAAI (Super Artistic AI) Factory Hackathon 2023, a program chaired by Christoph Faulhaber (Conceptual Artist, Film Maker) and Mohamed Elhoseiny (Computer Science Faculty at KAUST). About SAAI Factory Hackathon Established in 2021, SAAI Factory has been an initiative and one of the first contemporary art-based hackathons on Artificial Intelligence. The hackathon provides a framework for hackers, artists, coders, designers, tech evangelists, creatives, and developers from all disciplines to come together and explore the possibilities opened up by

Bioinspired Subsea Robots and Underwater Networks

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B18 Fellowship Hall

Featured Sessions
Abstract Advanced subsea robots and monitoring networks have become increasingly important for subsea exploration, environmental observation, industrial control, and maritime security. These systems face challenges ranging from shallow water to deep-sea applications. To overcome these challenges, engineers can draw inspiration from nature. Studies on the bio-sonar of dolphins provide a clue for reliable underwater communication and positioning, which is the basis for modern subsea networks and collaborative underwater robots. Pressure-resistant electronic design enables devices to operate at

Marine Autonomy: Research and Applications

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B18 Fellowship Hall

Featured Sessions
Abstract Marine autonomy does not have as many constraints as there are on land but there are still challenges that need to be overcome to make the most of current and future opportunities. My talk will concentrate on the research works done in the marine autonomous systems research group at Plymouth where the focus of the research group has been towards application of AI techniques to the navigation, guidance and control of autonomous marine vehicles. The talk will also discuss future challenges in the marine autonomy areas and their likely solutions. Biography Dr Sanjay Sharma, Associate

Creating Cognitive Enterprises Through Decision Intelligence

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B9 L2 H2 H2

It is difficult to predict the future, but ultimately, what matters is creating the future we want to live in. Our past and present are the product of our previous decisions. The decisions we make today will pave the way for the future. It is important that the right decision is made at the right time leading to the right outcome, paving the way towards the desired future.

The Prospect of Generative Language Processing

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B9 L2 H2 H2

In the evolving landscape of artificial intelligence, generative models are revolutionizing our interface with computational systems and reshaping societal paradigms. For example, foundation models have the potential to transform content creation across languages, offering discovery and productivity pathways for humans to engage with one another and their environment. This talk sketches the core methodologies propelling this groundbreaking progress, charting a grand vision for generative natural language processing.

Adaptive Optimization Algorithms for Machine Learning

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B1 L4 R4214

Machine learning assumes a pivotal role in our data-driven world. The increasing scale of models and datasets necessitates quick and reliable algorithms for model training. This dissertation investigates adaptivity in machine learning optimizers.

Resistance is Futile: Balancing Opportunities and Security Challenges of Federated Learning

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B4 L5 R5220

The rapid growth of Artificial Intelligence (AI) and Deep Learning mirrors an infectious phenomenon. While AI systems promise diverse applications and benefits, they bear substantial security and privacy risks. Indeed, AI represents a goldmine for the security and privacy research domain.

Data, AI and Cybersecurity: Synergies and Interconnection

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B9 L2 H2 H2

This Dean's Distinguished Lecture is part of the ECE Graduate Seminar. Modern Artificial Intelligence (AI) technologies, led by deep learning, have gained unprecedented momentum over the past decade.

Thoughts About Machine Learning - Week 7

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B9 LH1 R2322

The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence.

Thoughts About Machine Learning - Week 8

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B9 H1 R 2322

The AI Initiative, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division are delighted to announce the resumption of a series of seminars titled “Thoughts About Machine Learning". The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence. Attendance is

Averaging, Momentum, and Schedulers in Optimization for Deep Learning

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B9 L3 R3128

In this talk, I will present some work in progress on practical optimization methods for deep learning. We will start with a discussion of several empirical techniques that enable training of large-scale models in language and vision tasks, including weight decay, averaging, and schedulers. We will then look at a new approach that we call schedule-free due to its ability to work without a pre-defined time horizon. I will share some details about the theory for these methods, explain why they might be useful in practice and then shed some light on their limitations. This talk will be oriented towards people who already have some knowledge of optimization methods.

KAUST Invitational Workshop: Advancements in Data and Artificial Intelligence Tools for Transplantation and General Medicine

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B3 L5 R5220

KAUST Invitational Workshop: Advancements in Data and Artificial Intelligence Tools for Transplantation and General Medicine will bring together clinicians, scientists and experts in data analysis and machine learning/artificial intelligence to showcase their research and modern developments in kidney paired donation.

Computer vision: Teaching computers how to see the world

1 min read ·
Ghanem works in the Visual Computing Center (VCC) and is interested in computer vision, image processing, and machine learning.

Amin Allam (CEMSE PhD) wins the second prize in the DREAM Challenge 1B

1 min read ·
Amin Allam (CEMSE PhD student in the InfoCloud group) supervised by Prof Panos Kalnis has won the second prize in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1B. Amin Allam utilized Bayesian inference to achieve the 2nd place.

Professor Robert Hoehndorf's PhenomeNET method wins at Ontology matching workshop

1 min read ·
Professor Robert Hoehndorf and Postdoctoral Fellow Miguel Angel Rodriguez Garcia are the winners of the Ontology Alignment Evaluation Initiative Challenge sponsored by Pistoia Alliance.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Cocoon finally out: Professor Marco Canini together with VMware, Samsung, and Microsoft craft the ultimate SDN ally

2 min read ·
With the recent emergence of software-defined networking, which brings programmability and lowers the barrier for new functionalities into networks, the academic and industry communities have become very interested in the problem of network verification.

The awesome scope of big data

2 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Sarah Alghamdi defends her master's thesis

1 min read ·
Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

Maxat Kulmanov defends his thesis proposal

1 min read ·
Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

Alumni Focus: Ahmed Abdulmajeed Alabdulkarim, M.S. '11

1 min read ·
Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Azza Althagafi defends her Master's thesis

1 min read ·
Azza Althagafi, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 1, 2018.

KAUST Associate Professor Xiangliang Zhang talks about artificial intelligence

1 min read ·
Xiangliang Zhang, associate professor of computer science in the University's Computer, Electrical and Mathematical Science and Engineering division, recently gave an invited Early Career Spotlight Talk at the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI-18) 2018. During the event—which was held in Stockholm, Sweden, from July 13 to 19—Zhang and 21 international peers presented on all areas of artificial intelligence (AI).

We launch the first artificial intelligence (AI) tool for III-nitride semiconductors!

1 min read ·
Since the research of III-nitride LED and lasers started decades ago, the device design has been largely determined by deliberate device simulation or referring to prior arts. Recently, the activities of artificial intelligence (AI) through machine learning (ML) such as AlphaGo has prompted us to think about integrating this powerful technique into the LED and laser research to make the research process more efficient with the eventual goal of improving device performance.

Tweaking tools to track tweets over time

1 min read ·
Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.

KAUST Research Conference: AI in Medicine

1 min read ·

Student Focus: Adel Bibi

1 min read ·
KAUST Ph.D. student Adel Bibi, who works on bridging the gap between theory and practice in deep learning, believes we're living in a very exciting time.

KAUST Researchers Are the First to Generate Images of Realistic and Highly Detailed Texture Maps of Gigapixel in Size Using Deep Neural Networks

1 min read ·
KAUST researchers Anna Fruehstueck, Dr. Ibraheem Alhashim, and Prof. Peter Wonka have developed a novel technique to generate images of realistic and highly detailed texture maps using deep neural networks. The texture images synthesized by their system TileGAN can be of gigapixel size and are created by seamlessly merging smaller texture blocks into a single large image. The underlying neural networks are trained using high-resolution images such as detailed satellite imagery, maps and famous paintings.

AI learns complex gene-disease patterns

1 min read ·
A deep learning model improves the ability to identify genes potentially involved in disease.

Testing the blind spots in artificial intelligence

1 min read ·
Understanding the situations when artificial intelligence can fail is critical for application of future autonomous vehicles and medical diagnostics.

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

Symbol of change for AI development

1 min read ·
Bridging the knowledge gap in artificial intelligence requires an embedding function that helps step between different types of "thinking."

CEMSE New Faculty: Mohamed Elhoseiny, Assistant Professor of Computer Science

3 min read ·
Prior to joining the KAUST CEMSE Division earlier this year, Mohamed Elhoseiny received his Ph.D. degree from Rutgers University, New Brunswick in 2016, before spending over two years working as a postdoctoral researcher at Facebook in the company’s AI research wing. Elhoseiny joins the Division as an assistant professor of computer science based in the KAUST Visual Computing Center (VCC). He will also act as the PI of the KAUST Computer Vision, Content AI (Vision-CAIR) Research Group. Outside of his duties at KAUST, he is also acting as an artificial intelligence (AI) research consultant for Baidu Research, Silicon Valley AI Lab.

AI reveals nature of RNA-protein interactions

1 min read ·
A deep learning tool could help in structure-based drug discovery.

ActivityNet is featured in the 2019 Artificial Intelligence Index

1 min read ·
ActivityNet is featured in the AI Index as the benchmark for the algorithms that can recognize human actions and activities from videos.

LD Motif Finder locates ancient hidden protein patterns

1 min read ·
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

AI uses your imagination

1 min read ·
The psychology of human creativity helps artificial intelligence imagine the unseen.

Approximating a kernel of truth

1 min read ·
Machine learning tasks using very large data sets can be sped up significantly by estimating the kernel function that best describes the data.

CEMSE Division startups in focus: Peregrine Genomics

5 min read ·
The future has already arrived when it comes to the most exciting and promising field of modern medicine—precision medicine.

Flattening the sentimental curve

2 min read ·
-By Meres J. Weche, KAUST News As municipal, regional and national governments have mandated longer and more restrictive isolation time periods to slow down the spread of the COVID-19 pandemic and "flatten the curve," KAUST Associate Professor of Computer Science Xiangliang Zhang works on flattening the "emotional curve." Zhang's research focuses on developing reliable algorithms to analyze complex data sets through machine learning and artificial intelligence. Her research group specifically uses computer models to analyze social media posts on Twitter to study people's interests. When the

Peeling back the layers of deep machine learning

1 min read ·
A layer-based approach raises the efficiency of training artificial intelligence models.

KAUST CS student recognized with AAAI Outstanding Program Committee Award

4 min read ·
Konstantin Mishchenko, a Ph.D. student under the supervision of Professor Peter Richtárik, has been selected as one of the Association for the Advancement of Artificial Intelligence’s (AAAI) 12 Outstanding Program Committee Members for 2020. Mishchenko was presented with a certificate in recognition of his outstanding service at AAAI-20 held in New York from February 7-12.

Tracking the Curve: Analyzing the Emotional Response to COVID-19

1 min read ·
Hashtags like #covid19 and #coronavirus help us stay up to date on the developments of the new coronavirus pandemic. But beyond breaking news, tweets also offer a glimpse into the emotional side of the COVID-19 crisis.

AI that's built to save lives

1 min read ·
Associate Professor Xin Gao and his group have developed an artificial-intelligence (AI) based solution to help increase COVID-19 testing accuracy. Identifying cases of early stage infection has been particularly challenging for frontline clinicians. Gao's AI-based model, which aims to increase accuracy, has been put to immediate use at King Faisal Specialist Hospital (KFSH) in Riyadh.

KAUST Prospective Student: Siyuan Chen

2 min read ·
Siyuan Chen is a 22-year-old graduate who comes to KAUST from the School of Communication and Information Engineering, UESTC, China. Chen will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao.

Robert Hoehndorf promoted to associate professor at KAUST

4 min read ·
KAUST faculty member Robert Hoehndorf was recently promoted from the rank of assistant professor to associate professor. Hoehndorf’s promotion caps a year-long process where the German researcher’s scientific and scholarly output was measured and evaluated by internal and external reviews.

How AI helps improve COVID-19 testing

1 min read ·
Following a call by President Tony Chan for KAUST PIs to contribute through their research capabilities to alleviate the COVID-19 pandemic, efforts coordinated by Donal Bradley, KAUST vice president for research, and Pierre Magistretti, KAUST dean of the Biological and Environmental Science and Engineering division, mobilized a group of faculty to form the Rapid Research Response Team (R3T).

Former KAUST VSRP student wins Best Talk Award at ISMB 2020

2 min read ·
Runar Reve, a former KAUST Visiting Student Research Program (VSRP) student, recently received the International Society for Computational Biology’s (ISCB) Bio-Ontologies COSI – Best Talk Award at the 28th Conference on Intelligent Systems for Molecular Biology (ISMB) 2020.

Meet KAUST prospective student: Zhenwen Liang

1 min read ·
Zhenwen Liang, 22, is a computer science graduate from the University of Electronic Science and Technology of China (UESTC), China. Zhenwen will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

KAUST-Stanford neural network paper accepted for presentation at ECCV 2020

2 min read ·
A novel KAUST-Stanford University research paper by Panos Achlioptas (Stanford), Ahmed Abdelreheem (KAUST), Fei Xia (Stanford), Mohamed Elhoseiny (KAUST) and Leonidas Guibas (Stanford) was accepted for presentation at the 16th European Conference on Computer Vision (ECCV 2020).

Meet KAUST prospective student: Xiaopeng Xu

1 min read ·
Xiaopeng Xu is a computer science graduate who will join KAUST in the fall of 2020. Xu obtained a master’s degree in computer science from KAUST, and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. He will join KAUST in the fall of 2020 as a Ph.D. candidate and a member of the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao.

Meet KAUST prospective student: Pratik Nag

1 min read ·
Pratik Nag will join KAUST this fall as a Ph.D. candidate and member of the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Pratik holds a master's degree in statistical quality control and operation research, and a bachelor's degree in statistics, from the Indian Statistical Institute and the University of Calcutta, India.

Meet KAUST student: Konstantin Burlachenko

1 min read ·
Konstantin Burlachenko holds a master’s degree in computer science from Bauman Moscow State Technical University (BMSTU), Russia. After graduating from BMSTU, he worked in several prominent Moscow companies, such as Huawei, NVIDIA, and Yandex. He joined KAUST in August 2020 as a Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab.

Meet KAUST prospective student: Feng Liang

1 min read ·
Feng Liang is a computer science and engineering graduate who will join KAUST from the Southern University of Science and Technology (SUSTech), China. Feng will join the University this fall as a M.S. candidate and member of the KAUST Nanovisualization Research Group under the supervision of Professor Ivan Viola.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

KAUST startup tackles COVID using AI and robotics

1 min read ·
The COVID-19 pandemic has activated vital innovation and R&D initiatives to tackle the crippling effects, health ramifications, and overall impact of the coronavirus on global societies and economies. In recent months, the King Abdulaziz City for Science and Technology (KACST), based in Riyadh, Saudi Arabia, launched a COVID-19-themed virtual accelerator aimed at supporting talented innovators, aspiring entrepreneurs, and innovative small companies dedicated to finding solutions to mitigate the spread of the virus through protection, prevention, tracking/detection, and treatment solutions.

Broadening horizons for people with quadriplegia

1 min read ·
An assistive technology uses magnetic skin to support freedom of movement for people with quadriplegia.

Machine learning at speed

1 min read ·
Optimizing network communication accelerates training in large-scale machine-learning models.

Prof. Bernard Ghanem to give a talk at Machines Can See 2021

1 min read ·
The fifth annual international summit ‘Machines Can See’ will host IVUL's own Prof. Bernard Ghanem as a speaker. The summit, to be held on July 8th 2021, is hosted by VisionLabs, a leading software company devoted to developing products based on state-of-the-art Computer Vision algorithms. The summit will be a space for Computer Vision and Machine Learning leading experts to discuss technology trends and share experience regarding human-centric technologies. Together with Prof. Bernard Ghanem, the summit will feature Professors Dima Damen, Efstratios Gavves, Ira Kemelmacher-Shlizerman, and

Seven IVUL members recognized as Outstanding Reviewers by CVPR 2021

1 min read ·
The 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) has granted Outstanding Reviewer awards to seven members of IVUL: Alejandro Pardo, Ali Thabet, Chen Zhao, Mattia Soldan, Mengmeng Xu, Juan C. Pérez, and Silvio Giancola. CVPR, a premier annual computer vision event that will take place virtually from June 19th to June 25th, is the largest computer vision conference in the world, gathering researchers from academia and industry alike. The peer-review process through which the conference determines acceptance of papers relies on expert reviewers from across the world

IVUL paper Wins Best Poster award at CVPR Workshop

1 min read ·
A paper by IVUL members won a Best Poster award at the LatinX in Computer Vision Workshop at CVPR 2021, the world-renowned venue for research in Computer Vision. The paper, "BAOD: Budget-Aware Object Detection", was written by four IVUL members, Alejandro Pardo, Mengmeng Frost Xu, Ali K. Thabet, and Bernard Ghanem, in collaboration with Professor Pablo Arbeláez, from Universidad de los Andes. The paper studies the efficiency with which object-detection systems learn when constrained by limited budgets. We congratulate the authors!

SDAIA, KAUST sign MoU to develop AI research and innovation in Saudi Arabia

2 min read ·
Saudi Gazette report JEDDAH — The Saudi Data and Artificial Intelligence Authority (SDAIA) and King Abdullah University of Science and Technology (KAUST) have signed a memorandum of understanding to unite efforts to develop Artificial Intelligence (AI) research and modern technologies in the Kingdom. The MoU was signed by the CEO of the National Center for Artificial Intelligence, the innovative arm of SDAIA, Dr. Majid Al-Tuwaijri, and by KAUST President, Professor Tony Chan, the Saudi Press Agency reported on Thursday. The MoU supports science and technology research and improves its quality

KAUST research group win inaugural MCIT Digital Innovation Award

3 min read ·
A team of KAUST researchers have won first place in the Digital Research track at the inaugural Digital Innovation Awards held on Sunday, April 25, 2021. Launched by the Ministry of Communications and Information Technology (MCIT), the awards aim to “enhance the culture of innovation and entrepreneurship in digital research and development” in Saudi Arabia.

Divided attention could ease wireless congestion

1 min read ·
Predicting wireless traffic using artificial intelligence could improve the reliability of future wireless communications.

Drop the zeros for a performance boost

1 min read ·
An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

KAUST-KFUPM collaboration wins top honors at STC AIoT Hackathon

3 min read ·
A team composed of KAUST and King Fahd University of Petroleum and Minerals (KFUPM) students was one of three winning teams of this year’s Saudi Telecom Company (STC) AIoT (AI & IoT) Hackathon. The KAUST entry, AgriDoctor, a proposed autonomous, sustainable, intelligent agriculture system, was a standout pick for the competition judges from a pool of 110 project submissions and 350 participants.

Perovskites used to make efficient artificial retina

1 min read ·
KAUST researchers have built an artificial electronic retina that mimics the behaviour of the eye’s rod retina cells. Their artificial retina can recognise handwritten numbers.

Computer learns to write captions with feeling

1 min read ·
Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

Steering Customized AI Architectures for HPC Scientific Workloads: A Bird of Feather at SC22!

2 min read ·
Abstract: We explore the possibilities of a hybrid system capable of solving both HPC and AI scientific problems. Such a hybrid architecture combines the synergism between classical HPC platforms and dedicated AI chip systems, which is important due to the computational challenges brought to the fore by massively parallel Exascale systems. We discuss the system functionality, the algorithmic software adaptations, and performance considerations. We present efforts in supporting AI/ML applications in addition to seismic imaging, climate/weather prediction, and computational astronomy on hybrid

Saudi Pipeline International Conference and Exhibition

1 min read ·
Guang An Ooi attended the first Saudi Pipeline International Conference and Exhibition (SPICONX) 2022 held on 3 – 6 October in Dhahran, Saudi Arabia. He presented his work Hybrid Neural Network Cluster for Metallic Pipeline Inspection: A Big Data Approach, under the invaluable guidance of Professor Shehab Ahmed and Professor Hakan Bagci, as well as the cooperation of Dr. Moutazbellah Khater, Dr. Tarek Mostafa, and Dr. Mehmet Burak Ozakin. He had also the honor to be selected as one of the panelists to discuss pipeline inspection and modelling techniques and the applications of artificial

BORG team win at the KAUST-NVIDIA GPU Hackathon 2022

2 min read ·
Congratulations to Senay Kafkas, Maxat Kulmanov and Fernando Zhapa Camacho of the Bio-Ontology Research Group (BORG) on winning at the KAUST-NVIDIA GPU Hackathon 2022. The 8th edition of the Hackathon on Accelerating Scientific Applications using Graphical Processing Units (GPUs) was held at King Abdullah University of Science and Technology (KAUST), twelve teams of students and researchers enjoyed the opportunity to accelerate their codes in GPU’s under the guidance of expert mentors of NVIDIA, Codee, KAUST Supercomputing Lab (KSL) and KAUST core labs.

MS Thesis Defense | Learning 3D structures for protein function prediction

1 min read ·
Student Nurul Muttakin from the Bio-Ontology Research Group (BORG) under the supervision of Assoc. Prof. Robert Hoehndorf successfully defends his Masters thesis. He presented his research on the topic " Learning 3D structures for protein function prediction".

PhD Dissertation Defense | Ontology design patterns and methods for integrating phenotype ontologies

3 min read ·
CBRC PhD student Sarah Alghamdi successfully defended her dissertation last week. She has been an essential member of the Bio-Ontology Research Group (BORG) since 2015.Her research interests include the applications of artificial intelligence and statistical methods to genomics and healthcare.

PhD Dissertation Defense | Prioritizing Causative Genomic Variants by Integrating Molecular and Functional Annotations from Multiple Biomedical Ontologies

3 min read ·
CBRC PhD Student Azza Althagafi successfully defends her dissertation under the supervision of Associate Professor Robert Hoehndorf. She has been a member of the Bio-Ontology Research Group (BORG) since 2016. Her research interest lies in the crossroads of computer science and biology, leveraging AI techniques to explore the human genome's intricacies and its link to various diseases.

A new approach to rare disease diagnosis

2 min read ·
An innovative AI-based tool leverages varied symptom descriptions, along with evidence from the scientific literature and genomic datasets, to pinpoint disease-associated gene variants.

Safeguarding the right to be forgotten

2 min read ·
An open-source software can help align artificial intelligence applications in healthcare with data privacy regulations.
Montserrat Ramirez de Angel receiving the Best Pitch Award at IEEE 2024 Sensors Conference

Giving voice to the voiceless through assistive technology

2 min read ·
Artificial skin coupled with sensors and machine learning technology allows people with voice disorders to communicate using soundless mouth movements.

AI tool predicts function of unknown proteins

2 min read ·
Powered by logic and large language models, DeepGO-SE predicts the biological role of proteins and could be used to aid drug discovery.

Former CBRC PhD Student Ibrahim Alabdulmohsin Highlighted for AI Contributions on Saudi National TV

1 min read ·
Former CBRC PhD student Dr. Ibrahim Alabdulmohsin, now a senior research scientist at Google DeepMind in Zurich, was featured by the Josoor Program on Saudi National TV. The program, meaning "Bridges" in Arabic, highlighted Dr. Alabdulmohsin's journey from his education at the University of Nebraska, Lincoln, and Stanford University to his leadership roles at Aramco's Advanced Analytics team. After graduating from KAUST under the supervision of Prof. Xin Gao, he joined DeepMind in 2019, contributing to groundbreaking AI research. The 24-minute episode explores his career and contributions to the field. Dr. Alabdulmohsin's commitment to advancing AI underscores his pivotal role in shaping the future of technology.

Exciting News from LEAP2024!

1 min read ·
Exciting News from LEAP2024! We are thrilled to announce that our two innovative startup companies, Qawam and MOBiSENSE, participated in LEAP2024, showcasing their cutting-edge products and services. Qawam, known for its groundbreaking posture correction solutions, has now developed an app that utilizes sensors from Apple Airpods. This app eliminates the need for a separate clip-on device, providing consumers with a more convenient and seamless posture correction experience. MOBiSENSE specializes in extracting valuable insights for mobile network operators (MNOs) and communications services

Zhenwen Liang

Ph.D. Student, Computer Science
Zhenwen Liang is a M.S./Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Zhenwen obtained a bachelor's degree in computer science from the University of Electronic Science and Technology of China (UESTC), China. Research Interests Zhenwen's research interests include AI research and machine learning.

Ángel López Oriona

Postdoctoral Research Fellow, Statistics

Youssef S. Mohamed

Ph.D. Student, Computer Science
Education Youssef Mohamed is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Bachelor's Degree in Communication and Information Engineering, University of Science and Technology in Zewail City, Cairo, Egypt (2013-2018) Master's Degree in Computer Science, University of Tartu, Tartu, Estonia (2019-2022) Research Interests I am interested in developing AI that is aware of the Emotional and Cultural aspects of human intelligence. We study human

Victor Escorcia

Research Scientist committed to innovating AI agents to empower creativity and increase productivity. Passionate about developing cutting-edge AI applications involving video understanding and creative storytelling. I have a wide range of research and engineering interests from on-device AI to General AI. Reach out!

Tagreed Khalil

Tagreed Khalil joined Kaust in 2011, Initially, she have joined the Government Affairs department in November 2013 as an Administrative Assistant, thereafter, in January 2016, she continued with the Administrative Assistant role in the CEMSE division. Currently, she is working as a Business Administrator in the Artificial Intelligence (AI) Initiative, at King Abdullah University of Science and Technology (KAUST).

Soumia Si Youcef

Soumia Si Youcef is an electronics engineer and graduate student whose research focuses on designing innovative healthcare application devices.

Silvio Giancola

Silvio Giancola is a Research Scientist at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Bernard Ghanem in the Image and Video Understanding Laboratory (IVUL), part of the Center of Excellence for Generative AI (GenAI).

Shahad Qathan

M.S. Student (former), Computer Science
Shahad Qathan is an MS candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Artificial Intelligence, more specifically Machine Learning and its applications in healthcare and Bioinformatics Education Profile B.Sc, Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, 2020

Sarah Alghamdi

Ph.D. Student (former), Computer Science
Sarah Alghamdi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Sarah's research interests include applications of artificial intelligence and using statistical methods to genomics and healthcare. Professional Profile Program Committee for the International Conference of Biomedical Ontology (ICBO 2022) Program Committee for the International Society of Molecular Biology (ISMB 2022) - Bio-Ontology track 2018-2021: Teaching Assistant positions: CS

Sara Rojas Martinez

Sara's research focuses on NeRF and its applications, including scene editing and efficiency.

Sakhaa Alsaedi

Ph.D. (former), Computer Science

Safa Khemiri

Safa Khemiri is an MS/PhD candidate at the Communication Theory Lab (CTL), King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career In 2018 and after being ranked among the top 2% at the Tunisian national contest for entrance to engineering schools, Safa Khemiri joined the best engineering school in Tunisia, "Ecole Polytechnique de Tunisie (EPT)" where she received multidisciplinary training. In her final year, she specialized in signals and systems. Before joining KAUST for her Master’s degree, Safa had

Runar Reve

Visiting Student (former), Bio-Ontology Research Group
​Runar Reve is from Klepp, Rogaland, in Norway. He is currently in his industry year as a visiting student for his bachelor's degree in Artificial Intelligence and Robotics at Aberystwyth University in Wales and will obtain his degree by the summer of 2021. He is currently working with Professor Robert Hoehndorf Bio-Ontology Research Group (BORG) on genome-wide association study (GWAS) to find variants, or sets of variants, in genomes that can result in genetic disease.​ Research Interests Genome-Wide Association Studies​. Education ​Undergrad in Artificial Intelligence (with integrated year

Robert Hoehndorf

Associate Professor, Computer Science
Robert Hoehndorf is an Associate Professor of Computer Science at KAUST. His research focuses on bioinformatics, knowledge representation and reasoning, Semantic Web technologies, and neuro-symbolic methods.

Qizhou Wang

Qizhou Wang research focuses on advancing optical sensing, nanophotonics, and intelligent computational imaging.

Pratik Nag

Ph.D. Student, Statistics
Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Othman Soufan

Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent

Nojood Almayouf

Nojood Almayouf is a Graduate Research Assistant in the Distributed Systems and Autonomy (DSA) Lab within the Robotics, Intelligent Systems and Control (RISC) Research Initiative. Education M.S. in Electrical Engineering, KAUST, Saudi Arabia (Aug 2017 - Dec 2019). Visiting graduate student, Stanford University, United States (Jun 2017 - Aug 2017). B.S. (with first honor) in Electrical and Computer Engineering, Effat University, Saudi Arabia (Jan 2013 - Jun 2017). Professional Profile Graduate Teaching Assistant for ECE 271A/ME 221A: Linear Control Systems Course, KAUST (Aug 2022 - Dec 2022)

Noor Hussein

Noor Hussein is a dedicated learner in Machine Learning and Artificial Intelligence. She earned her undergraduate degree in Applied Mathematics and Statistics from Khalifa University (KU) in Abu Dhabi, UAE, with the highest honors. In the fall of 2022, Hussein embarked on her academic journey by joining the Master's program in Machine Learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi, UAE. She is expected to graduate from MBZUAI in the summer of 2024. During the summer of 2023, Hussein got the opportunity to join as a Visiting student at King Abdullah

Mohammed Abdullah Hussaini

Senior Embedded System Engineer at KAUST (King Abdullah University of Science and Technology)
KAUST-CEMSE-CS-Vision-CAIR-Mohamed-Elhoseiny

Mohamed Elhoseiny

Associate Professor, Computer Science
Professor Elhoseiny’s research focuses on developing affective artificial intelligence that understands and generates novel visual content. He has contributed to and led numerous seminal works of affective AI art creation.

Mohammed S. Alharbi

Maxat Kulmanov

Research Scientist (former), Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Maria Sara Nour SADOUN

Visiting Student (former), Information Science Lab
Research internship in King Abdullah University of Science and Technology within EMANG as a Visiting Student on the topic of Epileptic Seizure Detection using Electroencephalography (EEG).

Marco Canini

Professor, Computer Science
Professor Canini’s research seeks to improve networked-system design, implementation and operation concerning vital properties such as reliability, performance, security and energy efficiency.

Manal A. Alshehri

Ph.D. Student, Computer Science

Lia Coleman

Lia Coleman is a researcher and artist at VISION-CAIR, the Creative AI Research lab at the Visual Computing Center (VCC) of King Abdullah University of Science and Technology (KAUST). Education and Early Career Lia Coleman received her B.Sc. in Computer Science at Massachusetts Institute of Technology (MIT) in 2017. Since then, she has worked as a computer vision engineer, and is now researching novel art generation using GANs with Prof. Mohamed Elhoseiny of KAUST. In addition to her technical accomplishments, Lia is also a talented multidisciplinary artist. She has exhibited her work at MIT's

Klemens Katterbauer

Klemens Katterbauer is a AI specialist and software engineer working on robotics and artificial intelligence software development at EXPECARC in Saudi Aramco. His focus is on bridging the gap between sustainable energy transition development and the power of artificial intelligence. Klemens has published more than 50+ journal and conference publications, and has more than 20+ granted and submitted patents. Before this he was the IT Infrastructure Manager and Business Process Consultant for Eastern Europe, Middle East & Asia Pacific Region at the XXXLGroup (2nd largest furniture retailer in the

Khalil Allouch

Khalil Allouch is a MS/PhD student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education Khalil Allouch has studied mathematics and physics at the University of Tunis and has been among the top 1% in the Tunisian national contest of mathematics and physics in 2019. In 2022, he received his Engineering Diploma with first-class honors in multidisciplinary engineering from Ecole Polytechnique de Tunisie. Early Career Before joining the MS/Ph.D. program at KAUST, he had research experience with KAUST as a visiting

Kexin Niu

Ph.D. Student (former), Bioengineering
Kexin Niu, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Kexin obtained her bachelor's degree in Bioinformatics from Southern University of Science and Technology, Shenzhen, China. "What I like best about KAUST is the communication of diverse cultures and diverse disciplines, which enables me to have more possibilities." Research Interest Kexin’ s Research interests include but are not limited to bioinformatics and application of machine learning method in biomedical data. Education Profile 2016 -

Kerven Durdymyradov

Postdoctoral Research Fellow, Computer Science
Honors and Awards Bronze Medal and Honourable Mention- International Mathematical Olympiad (IMO). Netherlands–Kazakhstan (2011, 2010) 1st and 2nd place Winner - Open Doors International Olympiad - Mathematics & AI track. Russia (2022, 2020) Silver and Bronze Medals - Balkan Mathematical Olympiad (BMO). Romania-Moldova (2011, 2010) Silver and Bronze Medals - South Eastern Mathematical Olympiad (SEEMOUS). Romania-Greece (2014, 2013)

Jürgen Schmidhuber

Professor, Computer Science

Jinane Bazzi

Jesper Tegnér

Professor, Bioscience
Jasper Tegnér is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.

Haozhe Liu

Ph.D. Student, Computer Science
Haozhe Liu is a Ph.D. candidate at King Abdullah University of Science and Technology (KAUST), supervised by Prof. Jürgen Schmidhuber.
Hakim Ghazzai

Hakim Ghazzai

Senior Research Scientist, Electrical and Computer Engineering
Hakim is a researcher focusing on designing multi-disciplinary innovative solutions for diverse smart city applications and services.
gustavo soares valentim

Gustavo Soares Valentim

Dissemination Development of a Modular Open Source Power Electronics Didactic Platform L. Koleff et al. 2019 IEEE 15th Brazilian Power Electronics Conference and 5th IEEE Southern Power Electronics Conference (COBEP/SPEC), 2019

Frankly Toro

Frankly Toro is a Ms/Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career He is a graduate of National University of Colombia where he received a B.S. degree in Mechatronics. Education Profile B.S., Mechatronics , National University of Colombia, 2020

Ferdaous Tarhouni

Ferdaous Tarhouni is an MS/PhD student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education and Early Career Ferdaous has studied mathematics and physics at the Preparatory Institute for Engineering Studies of Tunis and has been ranked among the top 2% in the national engineering school exam in 2020. This enabled her to enter the Ecole Polytechnique de Tunisie (EPT), one of the most prestigious engineering schools in Tunisia where she received multidisciplinary training. In her final year, she specialized in

Fellipe Augusto Araújo Leandro

Fellipe received his bachelor degree in Electrical Engineering from Federal University of Rio Grande do Norte (UFRN), Brazil, in 2019. During his undergraduation, he worked in researches related to microcontroller-based embedded systems platforms, with focus in embedded software programming and PCB design. In 2019, he worked as an intern in a startup company developing embedded software for a Lora-based biotelemetry system, and also in another startup developing PCB design for low-cost Educational robotics and electronics. In 2020, he joined KAUST to research under the supervision of Professor

Fares Fourati

Fares Fourati is a Ph.D. student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini.

Feng Liang

Feng Liang is a former member of the Nanovisualization Research Group at KAUST lead by Professor Ivan Viola. Before joining KAUST, Feng obtained a bachelor's degree in computer science and engineering from the Southern University of Science and Technology (SUSTech), China. Research Interests Feng's research interests include artificial intelligence and computer graphics.

Fat-hy Omar Rajab

Master's Thesis Abstract The multi-agent robotic system has been proved to be more superior in undertaking functionalities, arduous or even impossible when performed by single agents. The increased efficiency in multi-agent systems is achieved by the execution of the task in a cooperative manner. But to achieve cooperation in multi-agent systems, a good localization system is an important prerequisite. Currently, most of the multi-agent systems rely on the use of the GPS to provide global positioning information which suffers great deterioration in performance in indoor applications, and also

Eslam Abdelrahman BAKR

Ph.D. Student, Computer Science
Eslam BAKR is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny. Education and Early Career BS in Computer Engineering, Cairo University,(Sep 2012-Jul 2017)Studied programming, computer architecture, electronics, communications, waves, and computer networks. M.Sc in Computer Engineering, Cairo University,(Mar 2019-May 2022) Work Experience: TA at Zewail City of Science and Technology, (Sep 2020-21) Instructor at Faculty of Computer Science, Ain

Dimitrios Kleftogiannis

Dimitrios Kleftogiannis obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group in King Abdullah University of Science and Technology (KAUST), in 2016. Research Interests Dimitrios works mostly on knowledge discovery technologies with applications in Bioinformatics and Computational Biology. ​His research interests include Machine Learning, Artificial Intelligence and Data Analysis of NGS-derived data. He has worked extensively on the prediction of distal DNA regulatory elements focusing on the category of enhancers. He is

Deng Luo

Ph.D. Student, Computer Science
Deng Luo is a Ph.D. Candidate in the Computer Science program under the supervision of Professor Ivan Viola at the Visual Computer Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia.

Chi-Han Peng

Research Scientist, Computer Science
Chi-Han Peng was a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). He is an Assistant Professor at the School of Information Science and Technology (SIST) at ShanghaiTech University. Education and Early Career Before joining KAUST, Dr. Peng was a postdoctoral research associate at University College London (UCL), working with Prof. Niloy Mitra. He worked at Nogle, a private company based in Taipei, on the 3D modeling and simulation of real-world clothes for the fashion/garment industries

Carlos Hinojosa

Education and Early Career o Bachelor of System Engineering and Informatics, University: Universidad Industrial de Santander, 2011-2015. o Master of Science in Computer and System Engineering, University: Universidad Industrial de Santander, 2016-2018. o PhD in Computer Science, University: Universidad Industrial de Santander, 2017-2022.

Bernard Ghanem

Professor Ghanem's research focuses on computer vision and machine learning, emphasizing large-scale video understanding, vision for automated navigation and theoretical foundations.

Azza Althagafi

Ph.D. Student (former), Computer Science
Azza Althagafi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and

Asmaa Abdallah

Amin Allam

Amin Allam obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Panagiotis Kalnis in 2017. Amin is a lecturer in the Computer Science department, Faculty of Computers and Information, Cairo University, Egypt. He got his bachelor's degree in Computers and Information in 2003 from Cairo University. Then, he got a Master's degree titled "An Arabic-Disambiguating Engine Based on Statistical Approaches" in Computer Science from the same university in 2008. Research Interest Amin's research interests include

Amer AlGhadhban

Amer AlGhadhban is a Ph.D. student under the supervision of Professor Basem Shihada at Networking Research Lab in King Abdullah University of Science and Technology (KAUST). Amer obtained both of his degrees B.S. and M.Sc. in Computer Engineering from King Fahd University of Petroleum and Minerals (KFUPM). He was a Cisco Academy instructor and during that he earned multiple professional certificates: Cisco Certified Network Associate (CCNA), Cisco wireless, SANS-GIAC Certified Firewall Analyst (GCFW-Gold), Certified Ethical Hacker (CEH), Security Certified Network Engineer (SCNP), and SANS

Alexandra Aguiar Gomes

Alexandra Aguiar Gomes has been a university educator since 1996. Her area of expertise is optimization, with current interests in the mathematics of decision-making for sustainable development and the mathematics of generative artificial intelligence. Alexandra finds joy in simplifying mathematical concepts, helping her students discover the practicality of mathematics in their fields.

Alejandro Pardo

Alejandro Pardo is a Ph.D. candidate in the Electrical and Computer Engineering (ECE) program under the supervision of Professor Bernard Ghanem at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia.

Ahmed El-Roby

Ahmed El-Roby obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). He got his Ph.D. in Computer Science from the David R. Cheriton School of Computer Science at the University of Waterloo. Currently, Ahmed is working as an Assistant Professor at the School of Computer Science at Carleton University in Ottawa, Ontario. Research Interests Ahmed's research interests included Data Integration, Automatic Linking, User Feedback, Artificial Intelligence, and

Ahmed Ishak Patwa

Visiting student at King Abdullah University of Science and Technology, Systems Engineer at Qawam for Research and Studies, Jeddah, Saudi Arabia.

Ahmed Abdulmajeed Alabdulkarim

Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Abdurauf Abdurakhmanov

Abdulrahman Katranji

Visiting Student (former), Electrical and Computer Engineering
Visiting student at the Information Science Lab (ISL) at King Abdullah University of Science and Technology.

Abdulmajeed Aljaloud

Visiting Student, Computer Science
Visiting Student Research Program ,Research Intern at King Abdullah University of Science and Technology (KAUST)
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Baptiste Py

Postdoctoral Research Fellow, Statistics
Dr. Baptiste Py is currently a post-doctoral research fellow in the Bayesian Deep Learning Group under the supervision of Professor Filippone where he is combining Mathematics, Bayesian Statistics, and Deep Learning to address real-life problems spanning energy, health, and food.

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).
Juan Jose Galeano Arenas

Juan José Galeano Arenas

Ph.D. Student, Statistics
Juan José Galeano Arenas is a MS/PhD student at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group.

Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation

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B5 L5 R5209

This thesis advances the computational efficiency of Vecchia approximation methods for Gaussian Processes (GPs), emphasizing GPU-based implementations for large-scale geospatial analysis and computer emulation. Traditional GPs require expensive covariance matrix inversions, which this work overcomes using scalable Vecchia-based approximations without sacrificing accuracy.

Collective Estimation of Multiple Bivariate Density Functions with Application to Angular-sampling-based Protein Structure Prediction

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B1

In this talk we develop a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs. Each log density function in the collection is modeled as a linear combination of a common set of basis functions. The shared basis functions are modeled as bivariate splines on triangulations and are estimated using data. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles.

Disease Risk Estimation by Combining Case-Control Data with Aggregated Information on the Population at Risk

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B1 L4 R4102

We propose a novel statistical framework by supplementing case–control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case–control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation.

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

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B9 H1 R2322

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Spatio-Temporal Data Analysis by Transformed Gaussian Processes

Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

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B2 L5 R5220

In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation

Truth, Knowledge, P-Values, Bayes, & Inductive Inference

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B1 L4 R4102

The talk will concern the role of statistical thinking in the search for Truth. This will bring us to a discussion of P-values, which has been, and still is, a central concept in Statistics and is a critical and highly controversial tool of scientific research. Recently it has elicited much, sometimes heated, debates and discussions. The American Statistical Association (ASA) was even compelled to release an official statement in early March 2016 regarding this issue, and a psychology journal gone to the extreme of banning the use of P-values in articles appearing in its journal!

Modern Techniques of Statistical Optimization for Machine Learning

Tong Zhang, Professor of Computer Science and Mathematics, HKUST

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B9 H1

Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through selected machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, and generative models.

Statistical models based on stochastic partial differential equations

Prof. David Bolin, Statistics, KAUST

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B9 L2 H1 R2322

The talk will give an overview of some recent developments of statistical models based on stochastic partial differential equations. We will in particular focus on equations with non-local differential operators or non-Gaussian driving noise, and explain when any why such models are useful. As motivating applications, analysis of longitudinal medical data and ocean waves will be considered.

Multivariate Functional Data Analysis and Visualization

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B3 L5 R5220

As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.

The KAUST 2022 Workshop on Statistics

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B2 B3 A0215

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

The KAUST 2023 Workshop on Statistics

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B18 R4203

The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

2 min read ·
Paola Crippa (presently at University of Newcastle, UK) is one of the winners of the highly contested 2015 L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

Brain waves in boxes

1 min read ·
A statistical method helps to identify abnormal signals in electroencephalograms and locate their source in the brain.

2015 Workshop on Computational Space-Time Statistics

1 min read ·
Organizers : Prof. Marc G. Genton , Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. Sudipto Banerjee, University of California Los Angeles, USA Prof. Souhaib Ben Taieb, Monash University, Australia Prof. Stefano Castruccio, Newcastle University, UK Prof. Miguel de Carvalho, Pontificia Universidad Catolica de Chile,​ Chile Prof. Clément Dombry, University of Franche-Comté , France Prof. Xuming He, University of Michigan, USA Prof. Hsin-Cheng Huang

Conference on Advances in Uncertainty Quantification Methods, Algorithm and Applications (#UQAW2016)

1 min read ·
The 4th consecutive year that KAUST is holding the UQAW annual event dedicated to reporting on the state-of-the-art and latest advances in the methods used to quantify the calculation uncertainties. The aim of the discussion forum in exchanging information on innovative algorithm design as well as their practical applications and performances in experimental settings.

Feeding in the moonlight

1 min read ·
Consistent around the world's ocean, the phases of the moon affect the upward migration of the world’s most abundant type of fish for feeding.

Postdoc Luigi Lombardo gets an Outstanding Student Poster and PICO EGU Award. Congratulations Luigi!

1 min read ·
The 2016 Outstanding Student Poster and PICO (OSPP) Award is awarded to Luigi Lombardo for the poster/PICO entitled Landslide triggering-thickness susceptibility, a simple proxy for landslide hazard? A test in the Mili catchment (North-Eastern Sicily, Italy). Regarding his work, Luigi said: " In the PICO presentation project we modeled the thickness at the crown of each landslide as the dependent variable. As a result, the predictive maps carried more information than classic susceptibility ones, enabling a better proxy to hazard prediction." You can find Luigi's poster here.

A clearer view of rainfall patterns

1 min read ·
Improved random modeling allows scientists to generate realistic patterns of high-frequency rainfall.

Getting a handle on extremes

1 min read ·
By tapping into the power of extreme value theory, an international team of researchers including Raphaël Huser from the University's Computer, Electrical and Mathematical Science and Engineering Division has developed a statistical model that overcomes the shortcomings of previous schemes to provide a reliable basis for climate research and the prediction of drought and flood. The model can accurately describe observed rainfall data and reliably predict the likelihood of future extreme events. The analysis of hourly or daily rainfall data presents many challenges for researchers and

Making space for climate simulations

1 min read ·
A statistics-based data compression scheme cuts data storage requirements for large-scale climate simulations by as much as 98 percent.

2016 Workshop Statistics for High-Dimensional & Complex Data

1 min read ·
Workshop on Statistics for High-Dimensional and Complex Data, Nov. 6-9, 2016​ Organizers : Prof. Marc G. Genton, Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. David Bolin, Chalmers University of Technology, Sweden Prof. Probal Chaudhuri, Indian Statistical Institute, India Prof. Dan Cooley, Colorado State University, USA​ Prof. Subhajit Dutta, IIT Kanpur, India Prof. Amanda Hering, Baylor University, USA Dr. Anna Kiriliouk, Université

Getting the jump on industrial failures

1 min read ·
A statistics-driven method for the early detection of emerging problems in industrial processes could improve industrial safety, reliability and productivity.

Keeping antennas at peak performance

1 min read ·
Sensitive detection of partial faults in antenna systems could prevent performance degradation in wireless networks.

A safe sweep of weather data

1 min read ·
A statistical technique for automatically cleaning erroneous data from weather-balloon observations will improve the accuracy of weather forecasting.

Improving connections for spatial analysis

1 min read ·
A statistical model that accounts for common dependencies in spatial data yields more realistic results for studies of temperature, wind and pollution levels.

Smart healthcare uses stats to spot a stumble

1 min read ·
Statistical monitoring technology can detect serious falls and immediately warn healthcare providers.

Going to extremes to predict natural disasters

1 min read ·
A systematic approach to selecting and configuring statistical models improves predictions of extreme events.

Letting the data speak for itself

1 min read ·
A new statistical approach for environmental measurements lets the data determine how to model extreme events.

Professor Sun receives the Environment Early Investigator Award 2017

2 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

Assistant Professor Ying Sun wins American Statistical Association award

1 min read ·
​​​​Ying Sun, KAUST assistant professor of applied ​mathematics and computational science​, won the 2017 Section on Statistics and the Environment (ENVR) Early Investigator Award from the American Statistical Association (ASA). Sun received her award at the 2017 Joint Statistical Meetings (JSM), held from July 29 to August 3 in Baltimore, Maryland, U.S. Sun's award recognizes her important contributions to the field of environmental statistics that help to address major problems like climate change and environmental pollution. At KAUST, Sun leads the Environmental Statistics Group in the

Dataset size counts for better predictions

1 min read ·
A statistical technique for modeling large datasets improves interpretation of climate and environmental data.

2017 Workshop Modern Statistics for Complex Data Structures

1 min read ·
2017 Workshop Modern Statistics for Complex Data Structures

Diagnosing the impacts of health policy

1 min read ·
Study design allows more rigorous evaluation of complex healthcare interventions.

One bad apple can spoil a robot swarm

1 min read ·
Coordinated robot swarms can achieve amazing feats, but even a single faulty unit can have serious consequences.

Modeling where the wind blows

1 min read ·
A new statistical technique could be used to more accurately locate global wind resources.

PhD student Sabrina Vettori selected for an ENAR Distinguished Student Paper Award. Congrats!

1 min read ·
PhD student Sabrina Vettori has been selected by the International Biometric Society Eastern North American Region (ENAR) to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting in Atlanta ( https://www.enar.org/meetings/spring2018/index.cfm ) for her manuscript entitled " Bayesian Hierarchical Modelling of Air Pollution Extremes Using Multivariate Max-Stable Processes ". She will present her work at an oral contributed paper session, be invited to attend the ENAR President’s reception and publicly receive their award at the beginning of the ENAR President’s Invited

Ph.D. Student Gaurav Agarwal received an Honorable Mention from ENVR Student Paper Competition

1 min read ·
Ph.D. student Gaurav Agarwal received an Honorable Mention from ENVR student paper competition for his paper entitled "Quantile function modeling with application to salinity tolerance analysis in plant data."

Best 2016 Paper in the Journal of Agricultural, Biological and Environmental Statistics

1 min read ·
Professor Huser will present the work in a special invited session (JABES showcase) at the International Biometric Conference taking place in Barcelona, Spain, in July 2018.

Revealing hidden relationships in data

1 min read ·
A method to visualize hidden statistical structure helps make sense of environmental data.

Making data more accessible

1 min read ·
Ying Sun is a multi-award-winning statistician who is inspired by the value of statistics in solving real-world problems.

Pulling rank on spatial statistics

1 min read ·
A technique that uses the power of computing could solve statistical problems cheaper and faster than current methods.

Challenging statistics of weather extremes

1 min read ·
More accurate statistical modeling of extreme weather will improve forecasting and disaster mitigation.

Zooming in on brain function

1 min read ·
Faster computations will allow researchers to see the finer details of brain activity in functional brain imaging.

Ph.D. Student Yuxiao Li won the ENAR Distinguished Student Paper Award

1 min read ·
KAUST Ph.D. student Yuxiao Li has been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

Second year Ph.D. student to receive top statistics award

1 min read ·
KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Ph.D. Student Yuxiao Li won the ENVR student paper award

2 min read ·
KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Point patterns help to predict landslides

1 min read ·
Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.

Connecting the dots to unlock brain structure

1 min read ·
Mapping genetic influences on connections between neural networks could lead to a better understanding of brain organization and behavior.

Making loudspeakers resonate like atoms

1 min read ·
Tiny loudspeakers can efficiently produce low-frequency sounds due to effects similar to the quantum physics governing atoms.

How extreme weather exacerbates air pollution

1 min read ·
Simultaneously modeling of air pollutants and weather under extreme conditions highlights the potential for serious health risks.

Searching for wind for the future

1 min read ·
The first quantification of wind energy in Saudi Arabia points to high wind power potential for many decades to come.

Reining in computational complexity

1 min read ·
A more efficient approach to modeling spatial data involving thousands of variables keeps computation time in check.

Solving real-world problems

1 min read ·
A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

2018 Workshop on Statistics and Data Science

1 min read ·
The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). Details on the workshop (speakers, abstracts and schedule) can be found here.

Stimulating deeper insights into brain function

1 min read ·
Modeling changes in brain activity over time provides deeper insights into learning and behavioral responses.

New stats apps show a virtual reality

1 min read ·
Harnessing the power of virtual reality will help to visualize data and improve statistical models.

Prof. Raphaël Huser appointed to serve as AE for JABES

1 min read ·
Prof. Raphaël Huser has been appointed to serve as an Associate Editor for the Springer Journal of Agricultural, Biological and Environmental Statistics ( JABES, 2017 Impact Factor: 1.072) starting from January 2019 . JABES publishes papers that introduce new statistical methods to solve practical problems in the agricultural sciences, the biological sciences, and the environmental sciences.

Raphaël Huser’s article among the top downloaded articles in Springer’s Environmental Sciences Journals

1 min read ·
Luigi Lombardo, Thomas Opitz, and Raphaël Huser’s article “Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster”, published in the scientific journal Stochastic Environmental Research and Risk Assessment (SERRA), in July 2018, is among the top downloaded articles in Springer’s Environmental Sciences Journals for the year 2018. Huser is an Assistant Professor of statistics in the CEMSE division and principal investigator of the Extreme Statistics (extSTAT) Research Group at KAUST.

Students Peng Zhong and Rustam Bekishev get their MS degree

1 min read ·
On December 14, 2018, KAUST welcomed 243 graduates at the University’s ninth Commencement ceremony. Among them, students Rustam Bekishev and Peng Zhong, who is now a Ph.D. student In Statistics in our group, got their MS degree. Congratulations!

Postdoc Carolina Euan received Sylvia Esterby Presentation Award from TIES

2 min read ·
Carolina Euán, a postdoctoral fellow in KAUST Associate Professor Ying Sun's Environmental Statistics research group, recently received the Sylvia Esterby Presentation Award from the International Environmentrics Society (TIES) at the 28th Annual TIES Conference 2018 held from July 16 to 21, 2018, in Guanajuato, Mexico. Euán won the award for her talk entitled "Bernoulli Vector Autoregressive Model with Applications to Spatio-temporal Drought Events in Mexico."

Prof. Birgir Hrafnkelsson visits the extSTAT group

1 min read ·
Professor Birgir Hrafnkelsson from the Department of Mathematics at the University of Iceland, will visit extSTAT during February 24-March 2, 2019. His research interests include Bayesian hierarchical modeling, spatial statistics, spatio-temporal modeling, extreme value analysis and statistical modeling of environmental data.

Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA book

1 min read ·
Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real

Clustering helps unlock secrets of the human brain

1 min read ·
The latest statistical methods from research on complex high-dimensional environmental data also yield powerful tools for interpreting brain activity.

Prof. Raphaël Huser now serving as AE of Econometrics and Statistics

1 min read ·
Prof. Raphaël Huser has been appointed to serve as an Associate Editor for the Econometrics and Statistics ( EcoSta) part B (Statistics) starting from May 2019.

Simple statistics can be good enough

1 min read ·
Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.

Taking aim at outliers

1 min read ·
Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.

Cutting datasets down to size

1 min read ·
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

Epilepsy study shows the shape of things to come

1 min read ·
Pyramidal graphs resulting from statistical analyses of EEG recordings can improve our understanding of epileptic seizures.

CEMSE Alumni: Mohammed Alsobay (Statistics Program)

1 min read ·
CEMSE Alumni: Mohammed Alsobay (Statistics Program)

Honing in on hotspots in spatial data

1 min read ·
More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

3 Faculty Positions in Statistics 2020 at KAUST

2 min read ·
The 3 faculty positions are in the Statistics Program (http://stat.kaust.edu.sa) within the Computer, Electrical, and Mathematical Sciences and Engineering Division. Currently, the Statistics Program has 7 core faculty and 10 affiliated faculty. We are primarily interested in applicants with strong background in one of the following areas: (1) Statistical Data Science and AI, including network data analysis and high-dimensional statistics (https://apply.interfolio.com/69165); (2) Statistical Climatology, with expertise in statistical analysis of climate model output data, in particular regional climate models, and in physical systems (https://apply.interfolio.com/69167); (3) Statistics for Public Health, including smart health data analysis, personalized medicine, and disease mapping (https://apply.interfolio.com/69168).

Marc Genton received an ISI Service Award 2019

3 min read ·
Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.

Shaking up the landslide model

2 min read ·
Areas without earthquake monitoring systems could model the tremors by using data from local landslides.

2019 Statistics and Data Science Workshop

1 min read ·
From November 18 to 20 the KAUST CEMSE Division has hosted the 2019 Statistics and Data Science Workshop. The workshop, organized by Workshop Chair Professor Ying Sun from the KAUST Environmental Statistics Group, featured 17 keynote talks from eminent data science researchers from Asia, Europe, North America and on-campus. Talks included: personalized recommendation by learning from heterogeneous graph data; dynamic spatial panel models; networks, common shocks, and sequential exogeneity; unbiased Markov chain Monte Carlo, plus many more.

Testing barley's salt tolerance is a numbers game

1 min read ·
Factors influencing the tolerance of barley to saline soils have been uncovered using an advanced robust statistical technique.

A second wind for turbine design

1 min read ·
Better prediction of extreme winds at sparsely observed locations could help optimize the design of wind farms.

Pop stats for big geodata

1 min read ·
A universal high-performance computing interface allows popular statistical tools to run efficiently on large geospatial datasets.

Prof. Raphaël Huser now serving as AE for the journal Environmetrics

1 min read ·
Prof. Raphaël Huser has been appointed to serve as an Associate Editor for Environmetrics, the Journal of The International Environmetrics Society ( TIES​), starting on March 16, 2020.

New Royal Statistical Society discussion paper

1 min read ·
The manuscript Linear mixed effects models for non-Gaussian continuous repeated measurement data by Ozgur Asar, David Bolin, Peter J Diggle, and Jonas Wallin has been accepted as a Royal Statistical Society discussion paper. The paper will be presented at the discussion meeting on May 13, 2020. These meetings are usually held at the Society's premises in Errol Street, London. However, due to the current situation it will instead be a virtual meeting. See the related URLs for further details about the event and paper, and how to submit a contribution to the discussion.

New paper accepted in Bayesian Analysis

1 min read ·
New paper accepted: Hrafnkelsson, B., Siegert, S., Huser, R., Bakka, H., and Jóhannesson, Á. V. (2020+), Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models, Bayesian Analysis, to appear [ arXiv].

Training agents to walk with purpose

1 min read ·
By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

A sharper view of flood risk

1 min read ·
Extreme weather patterns and regions at risk of flooding could be easier to spot using a new statistical model for large spatial datasets.

Most read paper of IMA Journal of Numerical Analysis

1 min read ·
The recently published article Numerical solution of fractional elliptic stochastic PDEs with spatial white noise is now the most read manuscript of IMA Journal of Numerical Analysis.

Blueprint may power up KSA's wind energy future

1 min read ·
High-resolution analysis of wind speed across Saudi Arabia can help fast track the expansion of the Kingdom’s emerging world-class wind energy industry.

New paper accepted in Earth Science Reviews (ESR)

1 min read ·
New paper accepted: Lombardo*, L., Opitz, T., Ardizzone, F., Guzzetti, F., and Huser, R. (2020+), Space-time landslide predictive modelling, Earth Science Reviews, to appear [PDF preprint]

Wind forecasts power up for reliable energy production

1 min read ·
Prediction of wind speed and direction for up to several hours in advance improves Saudi Arabia’s wind energy potential.

KAUST Environmental Statistics group publishes book exploring multivariate statistical analysis

2 min read ·
The KAUST Environmental Statistic (ES) research group has recently published a book titled "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches." The book is the outcome of the Core Research Grant (GRG) project led by KAUST Professor Ying Sun, Associate Professor of Statistics.

Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model

1 min read ·
New paper accepted: Hazra, A., and Huser, R. (2020+), Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model, Annals of Applied Statistics [ PDF preprint].

New paper accepted in Wiley Interdisciplinary Reviews (WIREs): Computational Statistics.

1 min read ·
New paper accepted: Huser, R. , and Wadsworth, J. L. (2020+), Advances in statistical modeling of spatial extremes , Wiley Interdisciplinary Reviews (WIREs): Computational Statistics, to appear [ PDF preprint ].

Today we celebrate World Statistics Day!

1 min read ·
Let’s discover the world of the statisticians at KAUST and be ready to be amazed by their cutting-edge research!

Sci-Café on Statistics

1 min read ·
Watch Sci-Café featuring panelists David Bolin, Hernando Ombao and Paula Moraga, with moderator Naadiya Carrim, by tuning in to the live Zoom feed, where you’ll be able to ask questions during the event, or by tuning in to the KAUST Official Facebook page or YouTube channel.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Modeling the neighborhood boosts landslide prediction

1 min read ·
A prediction model that considers multiple landslides over time in a given region may improve the accuracy of early warning systems.

KAUST STAT students come together for second ASA chapter meeting

2 min read ·
Members of the KAUST American Statistical Association (ASA) student chapter recently came together for the group’s second online meeting held on Tuesday, November 10, 2020. The meeting served as an orientation exercise for new KAUST Statistics (STAT) Program students while also highlighting the shared experience of STAT Ph.D. candidates: Jian Cao, Wanfang Chen, Yuxiao Li, and Gaurav Agarwal.

Modeling rainfall drop by drop

1 min read ·
A high-frequency model developed using data from new high-precision rain gauges gives fresh insight into the dynamics of rain and runoff events.

New paper published in JRSS Series A

1 min read ·
The paper Functional ANOVA modelling of pedestrian counts on streets in three European cities by David Bolin, Vilhelm Verendel, Meta Berghauser Pont, Ioanna Stavroulaki, Oscar Ivarsson, and Erik Håkansson has been published in the Journal of the Royal Statistical Society Series A.

Getting the inside track on street design

1 min read ·
Large-scale analysis of pedestrian data from three European cities examines how building density and street design influence pedestrian behavior.

A brainwave from social networks

1 min read ·
The statistics used to understand social networks reveal the diversity of functional connections in the brain.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Star light, star bright…as explained by math

1 min read ·
The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Mixing precision for model acceleration

1 min read ·
A mixed-precision approach for modeling large geospatial datasets can achieve benchmark accuracy with a fraction of the computational run time.

Beating the curse of dimensionality

1 min read ·
By scanning past data for both partial and complete matches to current observations, a KAUST-led research team has developed a prediction scheme that can more reliably forecast the future trajectory of environmental parameters. The collection of data at regular intervals over time is common in many fields but particularly so in environmental, transportation and biological research. Such data are used to monitor and record the current state and also to help predict what might come in the future. A typical approach is to look for previous patterns or trajectories in the data that match the

Trio of tuning tools for modeling large spatial datasets

1 min read ·
Statistical tools help fine-tune the parameters and approximations of models used to make sense of large spatial datasets.

Heatwave model shows it is hotter in more places, more often

1 min read ·
New modeling shows that heatwaves across Europe have increased in both frequency and spatial extent over the past century.

Deeper defense against cyber attacks

1 min read ·
To address the growing threat of cyberattacks on industrial control systems, a KAUST team including Fouzi Harrou, Wu Wang and led by Ying Sun has developed an improved method for detecting malicious intrusions.

Finding true protein hotspots in cancer research

1 min read ·
Novel statistical methods help tell the difference between "false positives" and true detections of protein domain hotspots that could be linked to cancer.

New article in NeuroImage

1 min read ·
The article Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups, which investigates the accuracy of SPDE-based models for cs-fMRI data, is now in press in NeuroImage.

Getting to the bottom of deadly weather

1 min read ·
Cold still causes far more deaths than heat in India. A study of the correlation between temperature and mortality in the Indian city of Pune has found that cold, rather than heat, is by far the bigger killer.

Competition sheds light on approximation methods for large spatial datasets

1 min read ·
Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Decrypting the interdependence of cryptocurrencies

1 min read ·
A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

New book chapter accepted

1 min read ·
New book chapter accepted: Hazra, A., Huser, R. , and Jóhannesson, Á. V. (2021+), Bayesian l atent Gaussian models for high-dimensional spatial extremes , In Statistical Modeling Using Bayesian Latent Gaussian Models – With Applications in Geophysics and Environmental Sciences, editor B. Hrafnkelsson, Springer, to appear [ arXiv ][ PDF preprint ].

New paper accepted

1 min read ·
New paper accepted: Zhong, P., Huser, R., and Opitz, T. (2022+), Exact simulation of max-infinitely divisible processes, Econometrics and Statistics [ arXiv][ PDF preprint ].

Can machine learning help predict disease spread?

1 min read ·
Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Finding Terra Incognita

1 min read ·
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.

Professor Bolin earns American Statistical Association Section on Statistics and the Environment Early Investigator Award

2 min read ·
Prof. David Bolin has been selected for the American Statistical Association Section on Statistics and the Environment Early Investigator (ENVR) Award for his outstanding contributions to environmental statistics.

Extreme analysis of seizures

1 min read ·
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Better health from better data

1 min read ·
The fast-developing science of geospatial statistics offers new insights into diverse health challenges, from cancer hotspots to problems of air pollution and the spread of COVID-19. Three new projects should yield a better understanding of what causes disease in Saudi Arabia and how to fight it. KAUST also stands to make a real contribution to the region by improving health in the Middle East and North Africa (MENA). The field of geospatial statistics and health surveillance, known as GeoHealth, uses data from multiple sources to create disease maps of geographical areas. When information is

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.

Zhuo Qu

Ph.D. Student, Statistics
Zhuo Qu is a PhD candidate in statistics, under Prof. Marc Genton. Zhuo likes to use statistics methods to interpret the data in the applications and find the new possibility of exploring the new statistics model. Being eager to learn and experience the uncertainty in the research and life, she also like chatting, swimming and piano. Her goal is to be a spare-time pianist among professional statisticians. Education and Early Career King Abdullah University of Science and Technology, Thuwal, January 2019 - December 2022, PhD in Statistics King Abdullah University of Science and Technology

Yuxiao Li

Ph.D., Statistics
Yuxiao Li obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. Education and Early Career: Yuxiao obtained his Bachelor of Science degree in Mathematics in 2013 from Beijing Institute of Technology, Beijing, China. In 2015, he graduated with his Master of Science degree in Statistics from the University of California at Irvine, USA. Then, he joined the Ph.D. program in Statistics at KAUST. Research Interest: His research interests lie in the fields such as

Xiran Zhang

Ph.D. Student, Statistics
Xiran Zhang research investigates statistics and high-performance computing, with a particular focus on scalable methods for large-scale geostatistical and spatio-temporal problems.

Wanfang Chen

Ph.D., Statistics
Wanfang Chen obtained a Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. Education and Early Career Wanfang Chen obtained her Bachelor Degree of Science in Mathematics at Beijing Institute of Technology (BIT) in China in 2013. She obtained her Master Degree of Science in Econometrics at Xiamen University in China in 2016. She joined the Ph.D. program in Statistics at KAUST in 2016. Research Interest Spatio-temporal statistics, wind

Sihan Chen

Ph.D. Student, Statistics

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Sagnik Mondal

Ph.D. Student, Statistics
Sagnik Mondal is a PhD student in the Statistics program at KAUST. Prior to joining KAUST, Sagnik completed a Master of Science degree from the Indian Institute of Technology (IIT), Kanpur, India.

Qilong Pan

Ph.D. Student, Statistics
Qilong Pan's research focuses on developing scalable statistical methods, with a particular emphasis on Gaussian Processes and GPU acceleration.

Mary Lai O. Salvana

Ph.D., Statistics

Marco Scavino

Marco Scavino is a Former Visiting Associate Professor​​ from Universidad de la República, Uruguay, at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Marco's research interests include Bayesian techniques for uncertainty quantification, statistical methods of experimental design, statistical procedures for model calibration, selection and validation, multivariate data analysis, statistical inference for stochastic processes, and stochastic modeling. Selected Publications A Laplace method for under

Kesen Wang

Ph.D. Student, Statistics
Kesen Want is a MS student in the Statistics program at KAUST. Prior to joining KAUST, Kesen completed a Bachelor of Science degree from the University of Maryland College Park in the USA.

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jian Cao

Ph.D., Statistics
Jian Cao obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. His research focused on Monte Carlo simulation, hierarchical matrices, tile-low-rank matrices, and solving high dimensional multivariate normal or Student-t probabilities. Education and Early Career Ph.D. degree from King Abdullah University of Science and Technology Master from Shanghai Jiaotong University Bachelor from University of Science and Technology of China

Israel Martínez-Hernández

Postdoctoral Research Fellow, Statistics
Israel Martínez-Hernández is a postdoctoral fellow at the Spatio-Temporal Statistics & Data Science Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Israel Martínez-Hernández received his Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in December 2017. He joined KAUST as a Postdoctoral Research Fellow in February 2018. Research Interest Functional Data Analysis, Functional Time Series, Long Memory Process, NonLinear Cointegration, Spatial Functional Data Analysis, Time Series Analysis

Hanan Alahmadi

Ph.D. Student, Statistics
Haakon Baka

Haakon Bakka

Postdoctoral Research Fellow (former), Statistics
Haakon Bakka is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Haakon received his PhD degree from the Norwegian University of Science and Technology in Trondheim, Norway. Research Interest My focus is on applied Bayesian statistics, often with spatial or space-time models, including priors, constructing random effects, fast inference, software development and data analysis.

Gulzhan Tumenbayeva

Ph.D. Student, Statistics
Gulzhan Tumenbayeva is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Gulzhan obtained her Bachelor of Science in Mathematics, Diploma with Honors in 2017 from Nazarbayev University, Astana, Kazakhstan. Then, she joined the MS/PhD program in Statistics at KAUST in 2017. In 2018, she graduated her Master of Science in Statistics at KAUST. Research Interest Her research interest is mainly in Bayesian and computational Statistics as well as

Fouzi Harrou

Senior Research Scientist, Statistics
Harrou’s research focuses on statistical decision theory and its applications, multivariate statistical process monitoring, anomaly detection and diagnosis.

Ercong Zhang

M.S. Student, Statistics
Ercong Zhang is a MS/Ph.D. student in the Statistics program at KAUST. Prior to joining KAUST, Ercong obtained his Bachelor of Science degree in Statistics from Southern University of Science and Technology (SUSTech), Shenzhen, China.
Hans Montcho profile

Hans Montcho

Postdoctoral Research Fellow, Statistics
Hans Montcho is a Post-doctoral fellow in the Bayesian Deep Learning Group.

Cristian Chiuchiolo

Cristian Chiuchiolo is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Christian obtained his Bachelor Degree in Statistics in 2015 at Florence, Italy. H e graduated with a Master of Science in Statistics at Bologna, Italy in 2017. Research Interest His research interest is mainly in Bayesian and computational Statistics as well as Probability and applications in R-INLA. Honors and Awards Cristian was announced a SAS Certified Base

Avetik Karagulyan

Postdoctoral Research Fellow, Optimization and Machine Learning
Avetik Karagulyan have joined the team of Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) as a Postdoctoral fellow in November 2021. Education and Early Career Avetik Karagulyan has defended his thesis at Center of Research in Economics and Statistics (CREST), Paris in June 2021 entitled as "Sampling with the Langevin Monte-Carlo" under the supervision of professor Arnak Dalalyan. In 2018, he received MSc Mathematics, Vision, Learning (MVA) diploma at ENS Paris-Saclay with highest honors. Avetik Karagulyan graduated from

Alibi Jangeldin

M.S. Student, Statistics
Alibi Jangeldin was an MSc in Statistical Data Science candidate in Spatio-Temporal Statistics Group in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Marc Genton. Education and Early Career Alibi obtained his bachelor degree in Applied Mathematics from Nazarbayev University in Kazakhstan in 2018. He also worked as a data scientist at Ernst & Young and General Prosecutor's Office of Kazakhstan. M.S. In Statistics from KAUST, Dec. 2019. Research Interest His research interest include data science, statistics and machine learning. Awards

Abdulhakim Qahtan

Abdulhakim Qahtan obtained his Ph.D. degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). Dissertation Title: Efficient Estimation of Dynamic Density Function with Applications in Streaming Data. First employment: Postdoctoral researcher at QCRI in Qatar. Research Interests Abdulhakim's research interests included Data mining, Statistics​​​ and Applying statistical models in machine learning. Selected Publications Basma Alharbi, Abdulhakim
<> Circuits (146)

Manufacturing and Integration Strategies for Flexible Electronics Across Form Factors

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B9 R2325

This talk presents manufacturing and integration strategies for flexible electronics across form factors, from E-LIG, a laser-enabled additive process for functional flexible printed circuit boards, to FiberCircuits, which embed complete circuits within fibers for interactive textiles.

On-chip antenna: Practical design and characterization considerations

1 min read ·
Atif Shamim, et al., "On-chip antenna: Practical design and characterization considerations." In 2010 14th International Symposium on Antenna Technology and Applied Electromagnetics & the American Electromagnetics Conference, 2010, 1. This paper highlights the challenges of an emergent field, namely, on-chip antenna design. Consistent with the RF System-on-Chip (SoC) concept, co-design strategy for circuits and on-chip antennas is described. A number of design and layout issues, arising from the highly integrated nature of this kind of systems, are discussed. The characterization difficulties

Higher dimensional models of cross-coupled oscillators and application to design

1 min read ·
Ahmed S. Elwakil, et al., "Higher dimensional models of cross-coupled oscillators and application to design." Journal of Circuits, Systems, and Computers 19, (04), 2010, 787. We present four-dimensional and five-dimensional models for classical cross-coupled LC oscillators. Using these models, sinusoidal oscillation condition, frequency and amplitude can be found. Further, undesired behaviors such as relaxation-mode oscillations and latchup can be explained and detected. A simple graphical design procedure is also described.

HP memristor mathematical model for periodic signals and DC

1 min read ·
Ahmed Radwan, et al., " HP memristor mathematical model for periodic signals and DC". In 2010 53rd IEEE International Midwest Symposium on Circuits and Systems, 2010, 861. In this paper mathematical models of the HP Memristor for DC and periodic signal inputs are provided. The need for a rigid model for the Memristor using conventional current and voltage quantities is essential for the development of many promising Memristors' applications. Unlike the previous works, which focuses on the sinusoidal input waveform, we derived rules for any periodic signals in general in terms of voltage and

A best-first tree-searching approach for ML decoding in MIMO system

1 min read ·
Chung-An Shen, et al., "A best-first tree-searching approach for ML decoding in MIMO system." In Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010, 3533. In MIMO communication systems maximum-likelihood (ML) decoding can be formulated as a tree-searching problem. This paper presents a tree-searching approach that combines the features of classical depth-first and breadth-first approaches to achieve close to ML performance while minimizing the number of visited nodes. A detailed outline of the algorithm is given, including the required storage. The effects of

Time domain oscillating poles: stability redefined in memristor based Wien-oscillators

1 min read ·
Abdul Hafiz Talukdar, et al., "Time domain oscillating poles: stability redefined in memristor based Wien-oscillators." In 2010 International Conference on Microelectronics, 2010, 288. Traditionally, the necessary and sufficient condition for any system to be oscillating is that its poles are located on the imaginary (jω) axis. In this paper, for the first time, we have shown that systems can oscillate with time-domain oscillating poles. The idea is verified using a Memristor based Wien oscillator. Sustained oscillations are observed without having the poles of the system fixed on the

Nonlinear analysis of ring oscillator circuits

1 min read ·
Xiaoqing Ge, et al., "Nonlinear analysis of ring oscillator circuits." In Proceedings of the 2010 American Control Conference, 2010, 1772. Using nonlinear systems techniques, we analyze the stability properties and synchronization conditions for ring oscillator circuits, which are essential building blocks in digital systems. By making use of its cyclic structure, we investigate local and global stability properties of an n-stage ring oscillator. We present a sufficient condition for global asymptotic stability of the origin and obtain necessity if the ring oscillator consists of identical

Millimeter-Wave and Terahertz Technologies-Enhanced Plasma Wave Detection of Terahertz Radiation Using Multiple High Electron-Mobility Transistors Connected in Series

1 min read ·
Tamer Elkhatib et al., "Enhanced Plasma Wave Detection of Terahertz Radiation Using Multiple High Electron-Mobility Transistors Connected in Series," in IEEE Transactions on Microwave Theory and Techniques, vol. 58, (2), 2010, 331 We report on enhanced room-temperature detection of terahertz radiation by several connected field-effect transistors. For this enhanced nonresonant detection, we have designed, fabricated, and tested plasmonic structures consisting of multiple InGaAs/GaAs pseudomorphic high electron-mobility transistors connected in series. Results show a 1.63-THz response that is

An adaptive hybrid multiprocessor technique for bioinformatics sequence alignment

1 min read ·
Talal Bonny, et al., "An adaptive hybrid multiprocessor technique for bioinformatics sequence alignment." In 2010 5th cairo international biomedical engineering conference, 2010, 112. Sequence alignment algorithms such as the Smith-Waterman algorithm are among the most important applications in the development of bioinformatics. Sequence alignment algorithms must process large amounts of data which may take a long time. Here, we introduce our Adaptive Hybrid Multiprocessor technique to accelerate the implementation of the Smith-Waterman algorithm. Our technique utilizes both the graphics

Sinusoidal oscillators with lower gain requirements at higher frequencies based on an explicit tanh(x) nonlinearity

1 min read ·
Ahmed S. Elwakil, et al., "Sinusoidal oscillators with lower gain requirements at higher frequencies based on an explicit tanh (x) nonlinearity." International Journal of Circuit Theory and Applications 38, (7), 2010, 747. Two novel sinusoidal oscillator structures with an explicit tanh(x) nonlinearity are proposed. The oscillators have the attractive feature: the higher the operating frequency, the lower the necessary gain required to start oscillations. A nonlinear model for the two oscillators is derived and verified numerically. Spice simulations using AMS BiCMOS 0.35µ model parameters and

Design, implementation and analysis of fully digital 1-D controllable multiscroll chaos

1 min read ·
Abhinav Mansingka, et al., "Design, implementation and analysis of fully digital 1-D controllable multiscroll chaos" ICM 2011 Proceeding, 2011, 1. This paper introduces the fully digital implementation of a 1-D multiscroll chaos generator based on a staircase nonlinearity in the 3rd-order jerk system using the Euler approximation. For the first time, digital design is exploited to provide real-time controllability of (i) number of scrolls, (ii) position in 1-D space, (iii) Euler step size and (iv) system parameter. The effect of variations in these fields on the maximum Lyapunov exponent (MLE)

Fractional smith chart theory

1 min read ·
Atif Shamim, et al., "Fractional smith chart theory." IEEE Microwave and Wireless Components Letters 21, (3) , 2011, 117. For the first time, a generalized Smith chart is introduced here to represent fractional order circuit elements. It is shown that the standard Smith chart is a special case of the generalized fractional order Smith chart. With illustrations drawn for both the conventional integer based lumped elements and the fractional elements, a graphical technique supported by the analytical method is presented to plot impedances on the fractional Smith chart. The concept is then

Gain-enhanced LTCC system-on-package for automotive UMRR applications

1 min read ·
Farhan Abdul Ghaffar, et al., "Gain-enhanced LTCC system-on-package for automotive UMRR applications." In 2010 53rd IEEE International Midwest Symposium on Circuits and Systems, 2010, 934. Abstract: A novel Low Temperature Co-fired Ceramic (LTCC) based SoP for automotive radar applications is presented. For the first time a combination of a relatively low dielectric constant LTCC substrate and a high dielectric constant LTCC superstrate has been incorporated to enhance the overall gain of the module. The superstrate can provide additional protection to the integrated circuits (IC) in the harsh

On the mathematical modeling of memristors

1 min read ·
Radwan, Ahmed G., et al., "On the mathematical modeling of memristors." In 2010 International Conference on Microelectronics, 2010, 284. Since the fourth fundamental element (Memristor) became a reality by HP labs, and due to its huge potential, its mathematical models became a necessity. In this paper, we provide a simple mathematical model of Memristors characterized by linear dopant drift for sinusoidal input voltage, showing a high matching with the nonlinear SPICE simulations. The frequency response of the Memristor's resistance and its bounding conditions are derived. The fundamentals of

Nonlinear Analysis of Ring Oscillator and Cross-Coupled Oscillator Circuits

1 min read ·
Ge Xiaoqing, et al., "Nonlinear analysis of ring oscillator and cross-coupled oscillator circuits." Dynamics of Continuous, Discrete and Impulsive Systems Series B: Applications and Algorithms , 2010, 1. Hassan Khalil’s research results and beautifully written textbook on nonlinear systems have influenced generations of researchers, including the authors of this paper. Using nonlinear systems techniques, this paper analyzes ring oscillator and cross-coupled oscillator circuits, which are essential building blocks in digital systems. The paper first investigates local and global stability

Evaluation framework for K-Best sphere decoders

1 min read ·
Chung-An Shen, et al., "Evaluation framework for K-best sphere decoders." Journal of Circuits, Systems, and Computers 19 (05), 2010, 975. While Maximum-Likelihood (ML) is the optimum decoding scheme for most communication scenarios, practical implementation difficulties limit its use, especially for Multiple Input Multiple Output (MIMO) systems with a large number of transmit or receive antennas. Tree-searching type decoder structures such as Sphere decoder and K-best decoder present an interesting trade-off between complexity and performance. Many algorithmic developments and VLSI

Enhanced plasma wave detection of terahertz radiation using multiple high electron-mobility transistors connected in series

1 min read ·
Tamer Elkhatib, et al., "Enhanced plasma wave detection of terahertz radiation using multiple high electron-mobility transistors connected in series." IEEE transactions on microwave theory and techniques 58, (2), 2010, 331. We report on enhanced room-temperature detection of terahertz radiation by several connected field-effect transistors. For this enhanced nonresonant detection, we have designed, fabricated, and tested plasmonic structures consisting of multiple InGaAs/GaAs pseudomorphic high electron-mobility transistors connected in series. Results show a 1.63-THz response that is directly

Generalized model for memristor-based Wien family oscillators

1 min read ·
Abdul Hafiz Talukdar, et al., "Generalized model for memristor-based Wien family oscillators." Microelectronics Journal 42 (9), 2011, 1032. In this paper, we report the unconventional characteristics of Memristor in Wien oscillators. Generalized mathematical models are developed to analyze four members of the Wien family using Memristors. Sustained oscillation is reported for all types though oscillating resistance and time dependent poles are present. We have also proposed an analytical model to estimate the desired amplitude of oscillation before the oscillation starts. These Memristor-based

Design and implementation of a sort-free K-best sphere decoder

1 min read ·
Sudip Mondal, et al.,"Design and Implementation of a Sort-Free K-Best Sphere Decoder," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems 18, (10), 2010, 1497. This paper describes the design and very-large-scale integration (VLSI) architecture for a 4 × 4 breadth-first K-best multiple-input-multiple-output (MIMO) decoder using a 64 quadrature-amplitude modulation (QAM) scheme. A novel sort-free approach to path extension, as well as quantized metrics result in a high-throughput VLSI architecture with lower power and area consumption compared to state-of-the-art published

High performance technique for database applications using a hybrid gpu/cpu platform

1 min read ·
Mohamed Zidan, et al., "High performance technique for database applications using a hybrid gpu/cpu platform." In Proceedings of the 21st edition of the great lakes symposium on Great lakes symposium on VLSI, 2011, 85. Many database applications, such as sequence comparing, sequence searching, and sequence matching, etc, process large database sequences. we introduce a novel and efficient technique to improve the performance of database applications by using a Hybrid GPU/CPU platform. In particular, our technique solves the problem of the low efficiency resulting from running short-length

Analysis of bus width and delay on a fully digital signum nonlinearity chaotic oscillator

1 min read ·
Abhinav Mansingka, et al., "Analysis of bus width and delay on a fully digital signum nonlinearity chaotic oscillator." In 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS), 2011, 1. Abstract: This paper introduces the first fully digital implementation of a 3rd order ODE-based chaotic oscillator with signum nonlinearity. A threshold bus width of 12-bits for reliable chaotic behavior is observed, below which the system output becomes periodic. Beyond this threshold, the maximum Lyapunov exponent (MLE) is shown to improve up to a peak value at 16-bits and

Hardware Realization of Chaos Based Block Cipher for Image Encryption

1 min read ·
Mohamed L. Barakat, et al., "Hardware realization of chaos based block cipher for image encryption." In ICM 2011 Proceeding, 2011, 1. Unlike stream ciphers, block ciphers are very essential for parallel processing applications. In this paper, the first hardware realization of chaotic-based block cipher is proposed for image encryption applications. The proposed system is tested for known cryptanalysis attacks and for different block sizes. When implemented on Virtex-IV, system performance showed high throughput and utilized small area. Passing successfully in all tests, our system proved to be

High Dynamic Range Adaptive ΑΣ-Based Focal Plane Array Architecture

1 min read ·
Shun Yao, et al., "High Dynamic Range adaptive ΔΣ-based Focal Plane Array architecture." In 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011, 505. Abstract: In this paper, an Adaptive Delta-Sigma based architecture for High Dynamic Range (HDR) Focal Plane Arrays is presented. The noise shaping effect of the Delta-Sigma modulation in the low end, and the distortion noise induced in the high end of Photo-diode current were analyzed in detail. The proposed architecture can extend the DR for about 20N log2 dB at the high end of Photo

A Compact QPSK Modulator with Low Amplitude and Phase Imbalance for Remote Sensing Applications

1 min read ·
Farhan Abdul Ghaffar, et al., " A Compact QPSK Modulator with Low Amplitude and Phase Imbalance for Remote Sensing Applications" Canadian Journal on Electrical and Electronics Engineering 2, (4), 2011, 1. A new, compact and wide-band Quadrature Phase Shift Keying (QPSK) modulator is presented for remote sensing applications. The microstrip-based modulator employs quadrature hybrid coupler, Wilkinson divider, rat race coupler and GaAs MESFET switches. It is designed to be part of an X band remote sensing transmitter with a center frequency of 8.25GHz. The fabricated module demonstrates the

Random number generation based on digital differential chaos

1 min read ·
Mohamed Zidan, et al., "Random number generation based on digital differential chaos." In 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS), 2011, 1. In this paper, we present a fully digital differential chaos based random number generator. The output of the digital circuit is proved to be chaotic by calculating the output time series maximum Lyapunov exponent. We introduce a new post processing technique to improve the distribution and statistical properties of the generated data. The post-processed output passes the NIST Sp. 800-22 statistical tests. The system

Design and Comparison of LTCC Based Fractal Antennas

1 min read ·
Farhan Abdul Ghaffar, et al., "Design and comparison of LTCC based fractal antennas." In Proc. URSI Gen. Assem. Sci. Symp., 2011, 1. The paper presents design of three different fractal antennas in Low Temperature Co-fired Ceramic (LTCC) substrate. An aperture coupled feeding technique has been used in all the three designs which is highly suitable for multi-layer system on package (SoP) concept. The three fractal designs demonstrated are Sierpinski carpet fractal antenna, Sierpinski gasket fractal antenna and Koch snowflake fractal antenna. Bandwidths of 1.8 GHz (7.5%), 1.75 GHz (7.5%) and 1

Theory and Numerical Methods-Fractional Smith Chart Theory

1 min read ·
Atef Shamim, et al., "Fractional Smith Chart Theory," in IEEE Microwave and Wireless Components Letters 21, (3), 117. For the first time, a generalized Smith chart is introduced here to represent fractional order circuit elements. It is shown that the standard Smith chart is a special case of the generalized fractional order Smith chart. With illustrations drawn for both the conventional integer based lumped elements and the fractional elements, a graphical technique supported by the analytical method is presented to plot impedances on the fractional Smith chart. The concept is then applied

FAST GLOBAL SEQUENCE ALIGNMENT TECHNIQUE

1 min read ·
Talal Bonny, et al., "Fast global sequence alignment technique." In 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011, 1046. Bioinformatics database is growing exponentially in size. Processing these large amount of data may take hours of time even if super computers are used. One of the most important processing tool in Bioinformatics is sequence alignment. We introduce fast alignment algorithm, called `Alignment By Scanning' (ABS), to provide an approximate alignment of two DNA sequences. We compare our algorithm with the

Memristor-based relaxation oscillators using digital gates

1 min read ·
Moustafa A. Khatib, et al., "Memristor-based relaxation oscillators using digital gates." In 2012 Seventh International Conference on Computer Engineering & Systems (ICCES), 2012, 98. This paper presents two memristor-based relaxation oscillators. The proposed oscillators are designed without the need of any reactive elements, i.e., capacitor or inductor. As the "resistance storage" property of the memristor can be exploited to generate the oscillation. The proposed oscillators have the advantage that they can be fully integrated on-chip giving an area-efficient solution. Furthermore, these

The fractional-order modeling and synchronization of electrically coupled neuron systems

1 min read ·
Khaled Moaddy, et al., "The fractional-order modeling and synchronization of electrically coupled neuron systems" Computers and Mathematics with Applications 64 (10), 2012, 3329. Abstract: In this paper, we generalize the integer-order cable model of the neuron system into the fractional-order domain, where the long memory dependence of the fractional derivative can be a better fit for the neuron response. Furthermore, the chaotic synchronization with a gap junction of two or multi-coupled-neurons of fractional-order are discussed. The circuit model, fractional-order state equations and the

Impedance matching through a single passive fractional element

1 min read ·
Ahmed Radwan, et al., "Impedance matching through a single passive fractional element." In Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation, 2012, 1. For the first time, a generalized admittance Smith chart theory is introduced to represent fractional order circuit elements. The principles of fractional order matching circuits are described. We show that for fractional order α ; 1, a single parallel fractional element can match a wider range of load impedances as compared to its series counterpart. Several matching examples demonstrate the versatility of

Differential RF MEMS interwoven capacitor immune to residual stress warping

1 min read ·
Amro M. Elshurafa, , et al., "Differential RF MEMS interwoven capacitor immune to residual stress warping." Micro & Nano Letters 7 (7), 2012, 658. A RF MEMS capacitor with an interwoven structure is designed, fabricated in the PolyMUMPS process and tested in an effort to address fabrication challenges usually faced in MEMS processes. The interwoven structure was found to offer several advantages over the typical MEMS parallel-plate design including eliminating the warping caused by residual stress, eliminating the need for etching holes, suppressing stiction, reducing parasitics and providing

Fractional-order RC and RL circuits

1 min read ·
Ahmed Radwan, et al., "Fractional-order RC and RL circuits" Circuits, Systems, and Signal Processing 31 (6), 2012, 1901. This paper is a step forward to generalize the fundamentals of the conventional RC and RL circuits in fractional-order sense. The effect of fractional orders is the key factor for extra freedom, more flexibility, and novelty. The conditions for RC and RL circuits to act as pure imaginary impedances are derived, which are unrealizable in the conventional case. In addition, the sensitivity analyses of the magnitude and phase response with respect to all parameters showing the

Memristor-based mono-stable oscillator

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Bahgat, A. T., et al., "Memristor-based mono-stable oscillator." arXiv preprint , (2012). In this letter, a reactance-less mono-stable oscillator is introduced for the first time using memristors. By replacing bulky inductors and capacitors with memristors, the novel mono-stable oscillator can be an area-efficient solution for on-chip fully integrated systems. The proposed circuit is described, mathematically analysed and verified by circuit simulations.

Memristor: the illusive device

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Salama, Khaled Nabil. et al., "Memristor: the illusive device." In Proceedings of the great lakes symposium on VLSI, 2012, 208. The memristor (M) is considered to be the fourth two-terminal passive element in electronics, alongside the resistor (R), the capacitor (C), and the inductor (L). Its existence was postulated in 1971 but its first implementation was reported in 2008. Where was it hiding all that time and what can we do with it? Come and learn how the memristor completes the roster of electronic devices much like a missing particle that physicists seek to complete their tableaus.

The integrated satellite-acoustic telemetry (iSAT) system for tracking marine megafauna

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De la Torre Pedro, et al., "The integrated satellite-acoustic telemetry (iSAT) system for tracking marine megafauna." In 2012 Oceans-Yeosu, 2012, 1. This document describes the integrated satellite-acoustic telemetry (iSAT) system: an autonomous modular system for tracking the movements of large pelagic fish using acoustic telemetry and satellite communications. The sensor platform is described along with the propulsion and navigation systems. An application for tracking the whale shark (Rhincodon typus) in the Red Sea is included along with a discussion of the technical difficulties that such

Determination of maximum power transfer conditions of bimorph piezoelectric energy harvesters

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Mahmoud Al Ahmad, et al., "Determination of maximum power transfer conditions of bimorph piezoelectric energy harvesters." Journal of Applied Physics 111 (10), 2012, 102812 In this paper, a method to find the maximum power transfer conditions in bimorph piezoelectric-based harvesters is proposed. Explicitly, we derive a closed form expression that relates the load resistance to the mechanical parameters describing the bimorph based on the electromechanical, single degree of freedom, analogy. Further, by taking into account the intrinsic capacitance of the piezoelectric harvester, a more

Effect of boundary on controlled memristor-based oscillator

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Mohamed E. Fouda, et al., "Effect of boundary on controlled memristor-based oscillator." In 2012 International Conference on Engineering and Technology (ICET), 2012, 1. Abstract: Recently, the applications of memristors have spread into many fields and especially in the circuit theory. Many models have been proposed for the HP-memristor based on the window functions. In this paper, we introduce a complete mathematical analysis of the controlled reactance-less oscillator for two different window functions of Joglekar's model (linear and nonlinear dopant drift) to discuss the effect of changing

Fully Digital 1-D, 2-D and 3-D Multiscroll Chaos as Hardware Pseudo Random Number Generators

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Abhinav Mansingka, et al., "Fully digital 1-D, 2-D and 3-D multiscroll chaos as hardware pseudo random number generators." In 2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS), 2012, 1180. This paper introduces the first fully digital implementation of 1-D, 2-D and 3-D multiscroll chaos using the sawtooth nonlinearity in a 3rd order ODE with the Euler approximation. Systems indicate chaotic behaviour through phase space boundedness and positive Lyapunov exponent. Low-significance bits form a PRNG and pass all tests in the NIST SP. 800-22 suite without post

Two-layer radio frequency MEMS fractal capacitors in PolyMUMPS for S-band applications

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Amro Elshurafa, et al., "Two-layer radio frequency MEMS fractal capacitors in PolyMUMPS for S-band applications." Micro & Nano Letters 7, (5), 2012, 419. In this Letter, the authors fabricate for the first time MEMS fractal capacitors possessing two layers and compare their performance characteristics with the conventional parallel-plate capacitor and previously reported state-of-the-art single-layer MEMS fractal capacitors. Explicitly, a capacitor with a woven structure and another with an interleaved configuration were fabricated in the standard PolyMUMPS surface micromachining process and

Low-voltage puzzle-like fractal microelectromechanial system variable capacitor suppressing pull-in

1 min read ·
Amro M. Elshurafa, et al., "Low-voltage puzzle-like fractal microelectromechanial system variable capacitor suppressing pull-in" Micro & Nano Letters 7, (9), 2012, 965. Abstract: This Letter introduces an electrostatically actuated fractal MEMS variable capacitor that, by utilising the substrate, extends the tuning range (TR) beyond the theoretical limit of 1.5 as dictated by the pull-in phenomenon. The backbone concept behind the fractal varactor is to create a suspended movable plate possessing a specific fractal geometry, and to simultaneously create a bottom fixed plate complementary in

Two-layer radio

1 min read ·
Amro Elshurafa, et al., "Two-layer radio frequency MEMS fractal capacitors in PolyMUMPS for S-band applications." Micro & Nano Letters 7 (5), 2012, 419. In this letter, we fabricate for the first time MEMS fractal capacitors possessing two layers and compare their performance characteristics with the conventional parallel-plate capacitor and previously reported state-of-the-art singlelayer MEMS fractal capacitors. Explicitly, a capacitor with a woven structure and another with an interleaved configuration were fabricated in the standard PolyMUMPS surface micromachining process and tested at S

Non linear dynamics of memristor based 3rd order oscillatory system

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Arka Talukdar, et al., "Non linear dynamics of memristor based 3rd order oscillatory system." Microelectronics journal 43 (3), 2012, 169. In this paper, we report for the first time the nonlinear dynamics of three memristor based phase shift oscillators, and consider them as a plausible solution for the realization of parametric oscillation as an autonomous linear time variant system. Sustained oscillation is reported through oscillating resistance while time dependent poles are present. The memristor based phase shift oscillator is explored further by varying the parameters so as to present

A Best-First Soft/Hard Decision Tree Searching MIMO Decoder for a 4×4 64-QAM System

1 min read ·
Chung-An Shen, et al., "A Best-First Soft/Hard Decision Tree Searching MIMO Decoder for a 4×4 64-QAM System" IEEE Transactions on Very Large Scale Integration (VLSI) Systems 20, (8), 2012, 1537. Abstract: This paper presents the algorithm and VLSI architecture of a configurable tree-searching approach that combines the features of classical depth-first and breadth-first methods. Based on this approach, techniques to reduce complexity while providing both hard and soft outputs decoding are presented. Furthermore, a single programmable parameter allows the user to tradeoff throughput versus BER

Microscale Electrostatic Fractional Capacitors using Reduced Graphene Oxide Percolated Polymer Composites

1 min read ·
Amro M Elshurafa, et al., "Microscale Electrostatic Fractional Capacitors using Reduced Graphene Oxide Percolated Polymer Composites" Applied Physics Letters 102, 2013, 232991. Abstract: We show that graphene-percolated polymer composites exhibit fractional capacitance response in the frequency range of 50 kHz–2 MHz. In addition, it is shown that by varying the loading of graphene within the matrix from 2.5% to 12%, the phase can be controllably tuned from −67° to −31°, respectively. The electrostatic fractional capacitors proposed herein are easy to fabricate and offer integration capability

On the short-term predictability of fully digital chaotic oscillators for pseudo-random number generation

1 min read ·
Ahmed G. Radwan, et al., "On the short-term predictability of fully digital chaotic oscillators for pseudo-random number generation." In 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS), 2013, 373. This paper presents a digital implementation of a 3rd order chaotic system using the Euler approximation. Short-term predictability is studied in relation to system precision, Euler step size and attractor size and optimal parameters for maximum performance are derived. Defective bits from the native chaotic output are neglected and the remaining pass the NIST SP

Secure Ds-CDMA spreading codes using fully digital multidimensional multiscroll chaos

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Abhinav S. Mansingka, et al., "Secure Ds-CDMA spreading codes using fully digital multidimensional multiscroll chaos." In 2013 IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS), 2013, 1334. This paper introduces a generalized fully digital hardware implementation of 1-D, 2-D and 3-D multiscroll chaos through sawtooth nonlinearities in a 3rd order ODE with the Euler approximation, wherein low-significance bits pass all NIST SP. 800-22 tests. The low-significance bits show good performance as spreading code for multiple-access DS-CDMA in AWGN and multipath environments

Parametric control on fractional-order response for Lü chaotic system

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Khaled Moaddy, et al., "Parametric control on fractional-order response for Lü chaotic system." In Journal of Physics: Conference Series, 423, (1), 2013, 012024. Abstract. This paper discusses the influence of the fractional order parameter on conventional chaotic systems. These fractional-order parameters increase the system degree of freedom allowing it to enter new domains and thus it can be used as a control for such dynamical systems. This paper investigates the behaviour of the equally-fractional-order Lü chaotic system when changing the fractional-order parameter and determines the

Fibonacci-based hardware post-processing for non-autonomous signum hyperchaotic system

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bhinav AMansingka, et al., "Fibonacci-based hardware post-processing for non-autonomous signum hyperchaotic system." In 2013 International Conference on IT Convergence and Security (ICITCS), 2013, 1. This paper presents a hardware implementation of a robust non-autonomous hyperchaotic-based PRNG driven by a 256-bit LFSR. The original chaotic output is post-processed using a novel technique based on the Fibonacci series, bitwise XOR, rotation, and feedback. The proposed post-processing technique preserves the throughput of the system and enhances the randomness in the output which is verified

Implantable intraocular pressure monitoring systems: Design considerations

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Muhammad Arsalan, et al., "Implantable intraocular pressure monitoring systems: Design considerations." In 2013 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO), 2013, 1. Design considerations and limitations of implantable Intraocular Pressure Monitoring (IOPM) systems are presented in this paper. Detailed comparison with the state of the art is performed to highlight the benefits and challenges of the proposed design. The system-on-chip, presented here, is battery free and harvests energy from incoming RF

ISAT: The mega-fauna acoustic tracking system

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De la Torre Pedro, et al., "ISAT: The mega-fauna acoustic tracking system." In 2013 MTS/IEEE OCEANS-Bergen, 2013, 1. The acoustic tracking module of the Integrated Satellite and Acoustic Telemetry (iSAT) system is discussed in detail. iSAT is capable of detecting the relative direction of an acoustic source by measuring the order of arrival (OOA) of the acoustic signal to each hydrophone in a triangular array. The characteristics of the hydrophones, the projector, and the target acoustic signal used for iSAT are described. Initially it is designed to study the movements of whale sharks

Modeling and fabrication of an RF MEMS variable capacitor with a fractal geometry

1 min read ·
Elshurafa, A. M., et al., "Modeling and fabrication of an RF MEMS variable capacitor with a fractal geometry". In 2013 IEEE International Symposium on Circuits and Systems, 2013, 2711. In this paper, we model, fabricate, and measure an electrostatically actuated MEMS variable capacitor that utilizes a fractal geometry and serpentine-like suspension arms. Explicitly, a variable capacitor that possesses a top suspended plate with a specific fractal geometry and also possesses a bottom fixed plate complementary in shape to the top plate has been fabricated in the PolyMUMPS process. An important

Generalized Hardware Post Processing Technique for Chaos-Based Pseudorandom Number Generators

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Mohamed L. Barakat, et al., "Generalized Hardware Post Processing Technique for Chaos-Based Pseudorandom Number Generators" ETRI Journal, 35, (3), 2013, 448. Abstract: This paper presents a generalized post‐processing technique for enhancing the pseudorandomness of digital chaotic oscillators through a nonlinear XOR‐based operation with rotation and feedback. The technique allows full utilization of the chaotic output as pseudorandom number generators and improves throughput without a significant area penalty. Digital design of a third‐order chaotic system with maximum function nonlinearity is

Improved Memristor-Based Relaxation Oscillator

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Ahmed G. Musad, et al., "Improved Memristor-Based Relaxation Oscillator" Microelectronics Journal Volume 44, (9), 2013, 814. Abstract: This paper presents an improved memristor-based relaxation oscillator which offers higher frequency and wider tunning range than the existing reactance-less oscillators. It also has the capability of operating on two positive supplies or alternatively a positive and negative supply. Furthermore, it has the advantage that it can be fully integrated on-chip providing an area-efficient solution. On the other hand, The oscillation concept is discussed then a

Fully digital jerk-based chaotic oscillators for high throughput pseudo-random number generators up to 8.77 Gbitss

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Abhinav S. Mansingka, et al., "Fully digital jerk-based chaotic oscillators for high throughput pseudo-random number generators up to 8.77 Gbitss" Microelectronics Journal 44, (9), 2013, 744. Abstract: This paper introduces fully digital implementations of four different systems in the 3rd order jerk-equation based chaotic family using the Euler approximation. The digitization approach enables controllable chaotic systems that reliably provide sinusoidal or chaotic output based on a selection input. New systems are introduced, derived using logical and arithmetic operations between two system

Memristor-based memory The sneak paths problem and solutions

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Mohammed A. Zidan, et al., "Memristor-based memory The sneak paths problem and solutions" Microelectronics Journal 44, (2), 2013, 176. Abstract: In this paper, we investigate the read operation of memristor-based memories. We analyze the sneak paths problem and provide a noise margin metric to compare the various solutions proposed in the literature. We also analyze the power consumption associated with these solutions. Moreover, we study the effect of the aspect ratio of the memory array on the sneak paths. Finally, we introduce a new technique for solving the sneak paths problem by gating

5.2-GHz RF Power Harvesting Module in 0.18 μm CMOS for Biomedical Implantable Sensors

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Mahmoud H. Ouda, et al., "5.2-GHz RF Power Harvesting Module in 0.18 μm CMOS for Biomedical Implantable Sensors" IEEE Transactions on Microwave Theory and Techniques 61, (5), 2013, 2177. Abstract: A first fully integrated 5.2-GHz CMOS-based RF power harvester with an on-chip antenna is presented in this paper. The design is optimized for sensors implanted inside the eye to wirelessly monitor the intraocular pressure of glaucoma patients. It includes a five-stage RF rectifier with an on-chip antenna, a dc voltage limiter, two voltage sensors, a low dropout voltage regulator, and MOSCAP based on

50V All-PMOS Charge Pumps Using Low-Voltage Capacitors

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Ahmed Emira, et al., "50V All-PMOS Charge Pumps Using Low-Voltage Capacitors" IEEE Transactions on Industrial Electronics 60, (10), 2013, 4683. Abstract: In this paper, two high-voltage charge pumps (CPs) are introduced. In order to minimize the area of the pumping capacitors, which dominates the overall area of the CP, high-density capacitors have been utilized. Nonetheless, these high-density capacitors suffer from low breakdown voltage, which is not compatible with the targeted high-voltage application. To circumvent the breakdown limitation, a special clocking scheme is used to limit the

Towards neuromorphic electronics: Memristors on foldable silicon fabric

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MT Ghoneim, et al., "Towards neuromorphic electronics: Memristors on foldable silicon fabric" Microelectronics Journal 45 (11), 2014, 1392. The advantages associated with neuromorphic computation are rich areas of complex research. We address the fabrication challenge of building neuromorphic devices on structurally foldable platform with high integration density. We present a CMOS compatible fabrication process to demonstrate for the first time memristive devices fabricated on bulk monocrystalline silicon (100) which is next transformed into a flexible thin sheet of silicon fabric with all

An integrated energy-efficient capacitive sensor digital interface circuit

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Hesham Omran, et al., "An integrated energy-efficient capacitive sensor digital interface circuit." Sensors and Actuators A: Physical 216, 2014, 43. In this paper, we propose an energy-efficient 13-bit capacitive sensor interface circuit. The proposed design fully relies on successive approximation algorithm, which eliminates the need for oversampling and digital decimation filtering, and thus low-power consumption is achieved. The proposed architecture employs a charge amplifier stage to achieve parasitic insensitive operation and fine absolute resolution. Moreover, the output code is not

Control and switching synchronization of fractional order chaotic systems using active control technique

1 min read ·
Ahmed G. Radwan, et al., "Control and switching synchronization of fractional order chaotic systems using active control technique." Journal of Advanced Research 5 (1), 2014, 125. This paper discusses the continuous effect of the fractional order parameter of the Lü system where the system response starts stable, passing by chaotic behavior then reaching periodic response as the fractional-order increases. In addition, this paper presents the concept of synchronization of different fractional order chaotic systems using active control technique. Four different synchronization cases are

Memristor Multi-Port Readout: A Closed-Form Solution for Sneak-Paths

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Mohamed Zidan, et al., "Memristor multiport readout: A closed-form solution for sneak paths." IEEE Transactions on Nanotechnology 13 (20), 2014, 274. In this paper, we introduce for the first time, a closed-form solution for the memristor-based memory sneak paths without using any gating elements. The introduced technique fully eliminates the effect of sneak paths by reading the stored data using multiple access points and evaluating a simple addition/subtraction on the different readings. The new method requires fewer reading steps compared to previously reported techniques, and has a very

A family of memristor‐based reactance‐less oscillators

1 min read ·
mohamed Zidan, et al., "A family of memristor‐based reactance‐less oscillators." International Journal of Circuit Theory and Applications 42 (11), 2014, 1103. In this paper, we present for the first time a family of memristor‐based reactance‐less oscillators (MRLOs). The proposed oscillators require no reactive components, that is, inductors or capacitors, rather, the ‘resistance storage’ property of memristor is exploited to generate the oscillation. Different types of MRLO family are presented, and for each type, closed form expressions are derived for the oscillation condition, oscillation

A 7.9 pJ/Step Energy-Efficient Multi-Slope 13-bit Capacitance-to-Digital Converter

1 min read ·
Hesham Omran, et al., "7.9 pJ/step energy-efficient multi-slope 13-bit capacitance-to-digital converter." IEEE Transactions on Circuits and Systems II: Express Briefs 61 (8), 2014, 589. In this brief, an energy-efficient capacitance-to-digital converter (CDC) is presented. The proposed CDC uses digitally controlled coarse-fine multi-slope integration to digitize a wide range of capacitance in short conversion time. Both integration current and frequency are scaled, which leads to significant improvement in the energy efficiency of both analog and digital circuitry. Mathematical analysis for

Ultrafast nonlinear response of gold gyroid three-dimensional metamaterials

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Petros Farah, et al., "Ultrafast nonlinear response of gold gyroid three-dimensional metamaterials." Physical Review Applied 2, (4), 2014, 044002. We explore the nonlinear optical response of three-dimensional gyroidal metamaterials, which show greater than tenfold enhancements compared to all other metallic nanomaterials as well as bulk gold. A simple analytical model for this metamaterial response shows how the reflectivity spectrum scales with the metal fill fraction and the refractive index of the material that the metallic nanostructure is embedded in. The ultrafast response arising from

Achieving nanoscale horizontal separations in the standard 2 μm PolyMUMPS process

1 min read ·
Amro M Elshurafa, et al., "Achieving nanoscale horizontal separations in the standard 2 μm PolyMUMPS process." Applied Nanoscience 4 (2), 2014, 241. This paper shares with the research community how to achieve, effectively and easily, lateral submicron separations in the standard 2 μm PolyMUMPS process without any fabrication intervention or post-processing, based on the oxide sidewall spacer technique. Thousands of nanoseparations were created and successfully tested by visual inspection and by a simple capacitance measurement. The lateral separations attained were less than 440 nm and

Interference mitigation for broadcast in hierarchical cell structure networks: Transmission strategy and area spectral efficiency

1 min read ·
Yuli Yang, et al., "Interference mitigation for broadcast in hierarchical cell structure networks: Transmission strategy and area spectral efficiency." IEEE Transactions on Vehicular Technology 63 (8), 2014, 3818. Abstract: In this paper, a hierarchical cell structure (HCS) is considered, where an access point (AP) broadcasts to local nodes (LNs) over orthogonal frequency subbands within a local cell located in a macrocell. Since the local cell shares the spectrum licensed to the macrocell, a given LN is interfered with by the macrocell user (MU)'s transmissions over the same subband. To

Hardware stream cipher with controllable chaos generator for colour image encryption

1 min read ·
Mohamed L. Barakat, et al., "Hardware stream cipher with controllable chaos generator for colour image encryption" IET Image Processing 8 (1), 2014, 33. This study presents hardware realisation of chaos-based stream cipher utilised for image encryption applications. A third-order chaotic system with signum non-linearity is implemented and a new post processing technique is proposed to eliminate the bias from the original chaotic sequence. The proposed stream cipher utilises the processed chaotic output to mask and diffuse input pixels through several stages of XORing and bit permutations. The

Compensated readout for high-density MOS-gated memristor crossbar array

1 min read ·
Mohamed Zidan, et al., "Compensated readout for high-density MOS-gated memristor crossbar array." IEEE Transactions on Nanotechnology 14 (1), 2014, 3. Leakage current is one of the main challenges facing high-density MOS-gated memristor arrays. In this study, we show that leakage current ruins the memory readout process for high-density arrays, and analyze the tradeoff between the array density and its power consumption. We propose a novel readout technique and its underlying circuitry, which is able to compensate for the transistor leakage-current effect in the high-density gated memristor

Insights on capacitive interdigitated electrodes coated with MOF thin films: Humidity and VOCs sensing as a case study

1 min read ·
Christos Sapsanis, et al., "Insights on capacitive interdigitated electrodes coated with MOF thin films: Humidity and VOCs sensing as a case study." Sensors 15 (8), 2015, 18153. A prototypical metal-organic framework (MOF), a 2D periodic porous structure based on the assembly of copper ions and benzene dicarboxylate (bdc) ligands (Cu (bdc)· xH 2 O), was grown successfully as a thin film on interdigitated electrodes (IDEs). IDEs have been used for achieving planar CMOS-compatible low-cost capacitive sensing structures for the detection of humidity and volatile organic compounds (VOCs)

Memristors Empower Spiking Neurons With Stochasticity

1 min read ·
Maruan Al-Shedivat, et al., "Memristors empower spiking neurons with stochasticity." IEEE journal on Emerging and Selected Topics in Circuits and Systems 5 (2), 2015, 242. Abstract: Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be

Spectrum band selection in delay-QoS constrained cognitive radio networks

1 min read ·
Yuli Yang, et al., "Spectrum band selection in delay-QoS constrained cognitive radio networks." IEEE Transactions on Vehicular Technology 64 (7), 2014, 2925. Abstract: In this paper, a cognitive radio (CR) network with multiple spectrum bands available for secondary users (SUs) is considered. For the SU's active spectrum-band selection, two criteria are developed. One is to select the band with the highest secondary channel power gain, and the other is to select the band with the lowest interference channel power gain to primary users (PUs). With the quality-of-service (QoS) requirement

A low‐power 802.11 ad compatible 60‐GHz phase‐locked loop in 65‐nm CMOS

1 min read ·
Hammad M. Cheema, et al., "A low‐power 802.11 ad compatible 60‐GHz phase‐locked loop in 65‐nm CMOS" Microwave and Optical Technology Letters 57 (3), 2015, 660. A 60‐GHz fundamental frequency phase locked loop (PLL) as part of a highly integrated system‐on‐chip transmitter with on‐chip memory and antenna is presented. As a result of localized optimization approach for each component, the PLL core components only consume 30.2 mW from a 1.2 V supply. A systematic design procedure to achieve high phase margin and wide locking range is presented. The reduction of parasitic and fixed capacitance

Wavy Channel TFT-Based Digital Circuits

1 min read ·
Amir N. Hanna, et al., "Wavy Channel TFT-Based Digital Circuits." IEEE Transactions on Electron Devices 63 (4), 2016, 1550. Abstract: We report a wavy channel (WC) architecture thin-film transistor-based digital circuitry using ZnO as a channel material. The novel architecture allows for extending device width by integrating vertical finlike substrate corrugations giving rise to 50% larger device width, without occupying extra chip area. The enhancement in the output drive current is 100%, when compared with conventional planar architecture for devices occupying the same chip area. The current

Statistical Analysis for Memristor Crossbar Memories

1 min read ·
Rawan Naous, et al., "Statistical Analysis for Memristor Crossbar Memories." Int. Journal of Unconventional Computing 12, 2016, 251. The inherent memory capabilities bound with compactness and scalability features pose the memristor as an ideal rival for conventional nonvolatile memories. Its use within a crossbar structure offers high-density storage and tight dimensionality. A fitting parameter in the midst of the trend towards nanoscale integration, where gateless memories are on the rise in an attempt to achieve further space savings. Nonetheless, the gate sacrifice comes at the expense of

Experimental Verification of on Chip CMOS Fractional-Order Capacitor Emulators

1 min read ·
Georgia Tsirimokou, et al., "Experimental verification of on-chip CMOS fractional-order capacitor emulators." Electronics Letters 52 (15) , 2016, 1298. The experimental results from a fabricated integrated circuit of fractional-order capacitor emulators are reported. The chip contains emulators of capacitors of orders 0.3, 0.4, 0.5, 0.6 and 0.7 with nano-Farad pseudo-capacitances that can be adjusted through a bias current. Two off-chip capacitors are used to set the bandwidth of each emulator independently. The chip was designed in Austria microsystems (AMS) 0.35μ CMOS.

Cloaking through cancellation of diffusive wave scattering

1 min read ·
Mohamed Farhat, et al., "Cloaking through cancellation of diffusive wave scattering." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, 2016, 20160276. A new cloaking mechanism, which makes enclosed objects invisible to diffusive photon density waves, is proposed. First, diffusive scattering from a basic core–shell geometry, which represents the cloaked structure, is studied. The conditions of scattering cancellation in a quasi-static scattering regime are derived. These allow for tailoring the diffusivity constant of the shell enclosing the object so

Memristor-based neural networks: Synaptic versus neuronal stochasticity

1 min read ·
Rawan Naous, et al., "Memristor-based neural networks: Synaptic versus neuronal stochasticity." Aip Advances 6, (11), 2016, 111304. In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switching behavior of the memristive elements is considered as the main source of stochasticity of its operation. Building on its inherent variability, the memristor is

A light responsive two-component supramolecular hydrogel: a sensitive platform for the fabrication of humidity sensors

1 min read ·
Suman Samai, et al., "A light responsive two-component supramolecular hydrogel: a sensitive platform for the fabrication of humidity sensors." Soft matter 12 (11), 2016, 2842. The supramolecular assembly of anionic azobenzene dicarboxylate and cationic cetyltrimethylammonium bromide (CTAB) formed a stimuli responsive hydrogel with a critical gelation concentration (CGC) of 0.33 wt%. This self-sustainable two-component system was able to repair damage upon light irradiation. Moreover, it was successfully employed in the fabrication of highly sensitive humidity sensors for the first time.

Compatibility analysis of 3D printer resin for biological applications

1 min read ·
Shilpa Siyashankar, et al., "Compatibility analysis of 3D printer resin for biological applications" Micro & Nano Letters, 11 (10), 2016, 654. The salient features of microfluidics such as reduced cost, handling small sample and reagent volumes and less time required to fabricate the devices has inspired the present work. The incompatibility of three-dimensional printer resins in their native form and the method to improve their compatibility to many biological processes via surface modification are reported. The compatibility of the material to build microfluidic devices was evaluated in

Flash μ-fluidics: a rapid prototyping method for fabricating microfluidic devices

1 min read ·
Ulrich Buttner, et al., "Flash μ-fluidics: a rapid prototyping method for fabricating microfluidic devices." Rsc Advances 6 (78), 2016, 74822. Microfluidics has advanced in terms of design and structures; however, fabrication methods are time-consuming or expensive relative to facility costs and equipment needed. This work demonstrates a fast and economically viable 2D/3D maskless digital light-projection method based on a stereolithography process. Unlike other fabrication methods, one exposure step is used to form the whole device. Flash microfluidics is achieved by incorporating bonding and

Pilot assisted readout for passive memristor crossbars

1 min read ·
Rawan Naous, et al., "Pilot assisted readout for passive memristor crossbars." Microelectronics Journal 54, 2016 48. The high demands for performance and energy efficiency pose significant challenges for computational systems. Memristor-based crossbar architectures are actively considered as vital rivals for the traditional solutions. Nonetheless, density and energy driven passive array structures, that lack a switching control per cell, suffer from sneak paths that limit the range of accurate operation of the crossbar array. In this paper, the crossbar array is treated as a communication

Matching properties of femtofarad and sub-femtofarad MOM capacitors

1 min read ·
Hesham Omran, et al., "Matching properties of femtofarad and sub-femtofarad MOM capacitors." IEEE Transactions on Circuits and Systems I 63 (6) ,2016, 763. Small metal-oxide-metal (MOM) capacitors are essential to energy-efficient mixed-signal integrated circuit design. However, only few reports discuss their matching properties based on large sets of measured data. In this paper, we report matching properties of femtofarad and sub-femtofarad MOM vertical-field parallel-plate capacitors and lateral-field fringing capacitors. We study the effect of both the finger-length and finger-spacing on

Direct mismatch characterization of femtofarad capacitors

1 min read ·
Hesham Omran, et al., "Direct mismatch characterization of femtofarad capacitors" IEEE Transactions on Circuits and Systems II: Express Briefs 63 (2), 2016, 151. Abstract: Reducing the capacitance of programmable capacitor arrays (PCAs), commonly used in analog integrated circuits, is necessary for low-energy applications. However, limited mismatch data are available for small capacitors. We report mismatch measurement for a 2-fF poly-insulator-poly (PIP) capacitor, which is the smallest reported PIP capacitor to the best of the authors' knowledge. Instead of using complicated custom on-chip

Wide-range adaptive RF-to-DC power converter for UHF RFIDs

1 min read ·
Mahmoud H. Ouda, et al., "Wide-range adaptive RF-to-DC power converter for UHF RFIDs." IEEE Microwave and Wireless Components Letters 26 (8), 2016, 634. A wide-range, differential, cross-coupled rectifier is proposed with an extended dynamic range of input RF power that enables wireless powering from varying distances. The proposed architecture mitigates the reverse-leakage problem in conventional, cross-coupled rectifiers without degrading sensitivity. A prototype is designed for UHF RFID applications, and is implemented using 0.18 μm CMOS technology. On-chip measurements demonstrate a

Single-readout high-density memristor crossbar

1 min read ·
Mohammed A. Zidan, et al., "Single-readout high-density memristor crossbar." Scientific reports 6, 2016, 18863. High-density memristor-crossbar architecture is a very promising technology for future computing systems. The simplicity of the gateless-crossbar structure is both its principal advantage and the source of undesired sneak-paths of current. This parasitic current could consume an enormous amount of energy and ruin the readout process. We introduce new adaptive-threshold readout techniques that utilize the locality and hierarchy properties of the computer-memory system to address the

Stochasticity Modeling in Memristors

1 min read ·
Rawan Naous, et al., "Stochasticity modeling in memristors." IEEE Transactions on Nanotechnology 15 (1), 2016, 15. Abstract: Diverse models have been proposed over the past years to explain the exhibiting behavior of memristors, the fourth fundamental circuit element. The models varied in complexity ranging from a description of physical mechanisms to a more generalized mathematical modeling. Nonetheless, stochasticity, a widespread observed phenomenon, has been immensely overlooked from the modeling perspective. This inherent variability within the operation of the memristor is a vital

Zinc Oxide Integrated Wavy Channel Thin-Film Transistor-Based High-Performance Digital Circuits

1 min read ·
Amir N. Hanna, , et al., "Zinc oxide integrated wavy channel thin-film transistor-based high-performance digital circuits." IEEE Electron Device Letters 37 (2), 2016, 193. Abstract: High-performance thin film transistor (TFT) can be a great driving force for display, sensor/actuator, integrated electronics, and distributed computation for the Internet of Everything applications. While semiconducting oxides, such as zinc oxide (ZnO), present promising opportunity in that regard, still wide area of improvement exists to increase the performance further. Here, we show a wavy channel (WC)

Integration of fractal biosensor in a digital microfluidic platform

1 min read ·
Yousof Mashraei, et al., "Integration of fractal biosensor in a digital microfluidic platform." IEEE Sensors Journal 16 (24), 2016, 8775. Abstract: The digital microfluidic (DMF) platform introduces many applications in biomedical assays. If it is to be commercially available to the public, it needs to have the essential features of smart sensing and a compact size. In this paper, we report on a fractal electrode biosensor that is used for both droplet actuation and sensing C-reactive protein (CRP) concentration levels to assess cardiac disease risk. Our proposed electrode is the first two

Ferroelectric Fractional‐Order Capacitors

1 min read ·
Agamaryat Agambayev, et al., " Ferroelectric Fractional‐Order Capacitors". ChemElectroChem, 4(11), 2017, 2807. Poly(vinylidene fluoride)‐based polymers and their blends are used to fabricate electrostatic fractional‐order capacitors. This simple but effective method allows us to precisely tune the constant phase angle of the resulting fractional‐order capacitor by changing the blend composition. Additionally, we have derived an empirical relationship between the ratio of the blend constituents and the constant phase angle to facilitate the design of a fractional‐order capacitor with a desired

3D‐Printed Disposable Wireless Sensors with Integrated Microelectronics for Large Area Environmental Monitoring

1 min read ·
muhamad F. Farooqui, et al., "3D‐Printed Disposable Wireless Sensors with Integrated Microelectronics for Large Area Environmental Monitoring". Advanced Materials Technologies, 2(8), 2017, 1700051. Large area environmental monitoring can play a crucial role in dealing with crisis situations. However, it is challenging as implementing a fixed sensor network infrastructure over large remote area is economically unfeasible. This work proposes disposable, compact, dispersible 3D‐printed wireless sensor nodes with integrated microelectronics which can be dispersed in the environment and work in

Performance analysis of fractional-order PID controller for a parabolic distributed solar collector

1 min read ·
Shahrazad Elmetennani, et al., " Performance analysis of fractional-order PID controller for a parabolic distributed solar collector". In 2017 IEEE AFRICON, 2017, 440. Abstract: This paper studies the performance of a fractional-order proportional integral derivative (FOPID) controller designed for parabolic distributed solar collectors. The control problem addressed in concentrated solar collectors aims at forcing the produced heat to follow a desired reference despite the unevenly varying solar irradiance. In addition to the unpredictable variations of the energy source, the parabolic solar

Experimental behavior evaluation of series and parallel connected constant phase elements

1 min read ·
Georgia Tsirimokou, et al., "Experimental behavior evaluation of series and parallel connected constant phase elements". AEU-International Journal of Electronics and Communications, 74, 2017, 5. Fractional-order capacitors are the core building blocks for implementing fractional-order circuits. Due to the absence of their commercial availability, they can be approximated through appropriately configured passive or active integer-order element topologies. Such a topology, constructed using Operational Transconductance Amplifiers (OTAs) and capacitors has been implemented in monolithic form

Localized surface plate modes via flexural Mie resonances

1 min read ·
Mohamed Farhat, et al., "Localized surface plate modes via flexural Mie resonances". Physical Review B, 95(17), 2017, 174201. Surface-plasmon polaritons are naturally generated upon excitation of metals with high-frequency electromagnetic waves. However, the concept of spoof plasmons has made it possible to generate plasmoniclike effects in microwave electrodynamics, magnetics, and even acoustics. Similarly, in this paper, the concept of localized surface plate modes (SPMs) is introduced. It is demonstrated that SPMs can be generated on a two-dimensional (clamped or stress-free) cylindrical

Fractal electrochemical microsupercapacitors

1 min read ·
Marinal K. Hota, et al., " Fractal electrochemical microsupercapacitors". Advanced Electronic Materials, 3(10), 20171700185. The first successful fabrication of microsupercapacitors (μ‐SCs) using fractal electrode designs is reported. Using sputtered anhydrous RuO2 thin‐film electrodes as prototypes, μ‐SCs are fabricated using Hilbert, Peano, and Moore fractal designs, and their performance is compared to conventional interdigital electrode structures. Microsupercapacitor performance, including energy density, areal and volumetric capacitances, changes with fractal electrode geometry

A 33fJ/Step SAR capacitance-to-digital converter using a chain of inverter-based amplifiers

1 min read ·
Hesham Omran, et al., "A 33fJ/Step SAR capacitance-to-digital converter using a chain of inverter-based amplifiers". IEEE Transactions on Circuits and Systems I: Regular Papers, 64(2), 2017, 310. Abstract: A 12 - bit energy-efficient capacitive sensor interface circuit that fully relies on capacitance-domain successive approximation (SAR) technique is presented. Analysis shows that for SAR capacitance-to-digital converter (CDC) comparator offset voltage will result in parasitic-dependent conversion errors, which necessitates using an offset cancellation technique. Based on the presented

Self-Biased Differential Rectifier with Enhanced Dynamic Range for Wireless Powering

1 min read ·
Mahmoud H. Ouda, et al., "Self-biased differential rectifier with enhanced dynamic range for wireless powering." IEEE Transactions on Circuits and Systems II: Express Briefs 64, (5), 2017, 515. A self-biased cross-coupled differential rectifier is proposed with enhanced power-conversion efficiency (PCE) over an extended range of input power. A prototype is designed for ultrahigh-frequency (UHF) 433-MHz radio-frequency power-harvesting applications and is implemented using a 0.18-μm CMOS technology. The proposed rectifier architecture is compared with the conventional cross-coupled rectifier

Transparent Flash Memory Using Single Ta2O5 Layer for Both Charge-Trapping and Tunneling Dielectrics

1 min read ·
Mrinal K. Hota, et al., "Transparent Flash Memory Using Single Ta2O5 Layer for Both Charge-Trapping and Tunneling Dielectrics." A CS applied materials & interfaces 9 (26), 2017, 21856. We report reproducible multibit transparent flash memory in which a single solution-derived Ta2O5 layer is used simultaneously as a charge-trapping layer and a tunneling layer. This is different from conventional flash memory cells where two different dielectric layers are typically used. Under optimized programming/erasing operations, the memory device shows excellent programmable memory characteristics with a

Experimental demonstration of fractional-order oscillators of orders 2.6 and 2.7

1 min read ·
Ahmed S. Elwakil, et al., "Experimental demonstration of fractional-order oscillators of orders 2.6 and 2.7". Chaos, Solitons & Fractals, 96, 2017, 160. The purpose of this work is to provide an experimental demonstration for the development of sinusoidal oscillations in a fractional-order Hartley-like oscillator. Solid-state fractional-order electric double-layer capacitors were first fabricated using graphene-percolated P (VDF-TrFE-CFE) composite structure, and then characterized by using electrochemical impedance spectroscopy. The devices exhibit the fractional orders of 0.6 and 0.74

Tunable fractional-order capacitor using layered ferroelectric polymers

1 min read ·
Agamyrat Agambayev, et al. "Tunable fractional-order capacitor using layered ferroelectric polymers." AIP Advances 7 (9), 2017, 095202. Pairs of various Polyvinylidene fluoride P(VDF)-based polymers are used for fabricating bilayer fractional order capacitors (FOCs). The polymer layers are constructed using a simple drop casting approach. The resulting FOC has two advantages: It can be easily integrated with printed circuit boards, and its constant phase angle (CPA) can be tuned by changing the thickness ratio of the layers. Indeed, our experiments show that the CPA of the fabricated FOCs can

A New Design Methodology for Time-Based Capacitance-to-Digital Converters (T-CDCs)

1 min read ·
Ali H. Hassan, et al., "A New Design Methodology for Time-Based Capacitance-to-Digital Converters (T-CDCs)". AEU-International Journal of Electronics and Communications 96, 2018, 238. This paper introduces a 9-bit time-based capacitance-to-digital converter (T-CDC). This T-CDC adopts a new design methodology for parasitic cancellation with a simple calibration technique. In T-CDCs, the input sensor capacitance is first converted into a delay pulse using a capacitance-to-time converter (CTC) circuit; then this delay signal is converted into a digital code through a time-to-digital converter

Electronically tunable fully integrated fractional-order resonator

1 min read ·
Georgia Tsirimokou., et al., "Electronically tunable fully integrated fractional-order resonator". I EEE Transactions on Circuits and Systems II: Express Briefs, 65(2), 2017, 166. A fully integrated implementation of a parallel fractional-order resonator that employs together a fractional-order capacitor and a fractional-order inductor is proposed in this brief. The design utilizes current-controlled operational transconductance amplifiers as building blocks, designed, and fabricated in AMS 0.35-μm CMOS process and based on a second-order approximation of a fractional-order differentiator

Guest Editorial: Special Issue on Large-Scale Memristive Systems and Neurochips for Computational Intelligence

1 min read ·
Alex Pappachen James, et al., "Guest Editorial: Special Issue on Large-Scale Memristive Systems and Neurochips for Computational Intelligence". IEEE Transactions on Emerging Topics in Computational Intelligence, 2(5), 2018, 320. Abstract: The papers in this special section explore the use of large scale memristive systems and neurochips for computational intelligence. The interest in the application of memristor device as a memory element, neuromorphic device, as a sensor and as a switch for programmable logic circuits has been growing in the last decade. The small size, ease of

Experimental Verification of a Fractional-Order Wien Oscillator Built Using Solid-State Capacitors

1 min read ·
Kartci, Aslihan, et al. "Experimental Verification of a Fractional-Order Wien Oscillator Built Using Solid-State Capacitors." 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2018, 544. A new type of fractional-order capacitors (FOCs) is fabricated using a molybdenum disulfide (MoS 2 )-ferroelectric polymer composite. The phase angle of this FOC's impedance remains constant between 100 Hz and 10MHz with only a small deviation of ±4 degrees. The performance of the fabricated FOCs is further tested using the well-known Wien oscillator. The main motivation of

An ultra-broadband single-component fractional-order capacitor using MoS2-ferroelectric polymer composite

2 min read ·
Agamyrat Agambayev, et al., "An ultra-broadband single-component fractional-order capacitor using MoS2-ferroelectric polymer composite". Applied Physics Letters, 113(9),2018, 093505. The phase angle of a fractional-order capacitor's (FOC) impedance has a constant value between −90°−90° and 0°0°. Maintaining this value over a broad frequency band is of utmost importance since it increases the applicability of the electrical circuit that employs the fractional-order capacitor (FOC). In this work, a molybdenum disulfide (MoS2)-ferroelectric polymer composite is used to design/fabricate an FOC

ASIC oriented comparative analysis of biologically inspired neuron models

1 min read ·
El-Maksoud, Ahmed J. Abd, et al. "ASIC oriented comparative analysis of biologically inspired neuron models." 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2018, 504. This paper introduces the hardware and the ASIC implementations of the four most popular biologically inspired neuron models. The models are quartic, Izhikevich, Hindmarsh Rose and Fitzhugh-Nagumo. Moreover, some approximate computing techniques are applied on these models to reduce the area and power consumption. In addition, ASIC implementations of these models and their approximate

CMOS-RC Colpitts oscillator design using floating fractional-order inductance simulator

1 min read ·
Kartci, Aslihan, et al. "CMOS-RC Colpitts oscillator design using floating fractional-order inductance simulator." IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2018, 905. This paper deals with CMOS fractional-order inductance (FoL) simulator design and its utilization in 2.75 th order Colpitts oscillator providing high frequency of oscillation. The proposed floating FoL is composed of two unity-gain current followers (CF±s), two inverting voltage buffers, a transconductor, and a fractional-order capacitor (FoC) of order 0.75, while the input intrinsic

A Comparative Study of Interdigitated Electrode and Quartz Crystal Microbalance Transduction Techniques for Metal–Organic Framework-Based Acetone Sensors

1 min read ·
Chappanda N. Karumbaiah, et al., "A Comparative Study of Interdigitated Electrode and Quartz Crystal Microbalance Transduction Techniques for Metal–Organic Framework-Based Acetone Sensors". Sensors 18(11), 2018, 3898. We present a comparative study of two types of sensor with different transduction techniques but coated with the same sensing material to determine the effect of the transduction mechanism on the sensing performance of sensing a target analyte. For this purpose, interdigitated electrode (IDE)-based capacitors and quartz crystal microbalance (QCM)-based resonators were coated with

Fully printed 3D cube-shaped multiband fractal rectenna for ambient RF energy harvesting

1 min read ·
Azamat Bakytbekov, A, et al., "Fully printed 3D cube-shaped multiband fractal rectenna for ambient RF energy harvesting". Nano energy, 53, 2018, 587. The Internet of Things (IoT) is an emerging paradigm that requires billions of wirelessly connected devices in a complex radio-frequency (RF) environment. With such a huge number of devices in this scenario, recharging or replacing batteries becomes impractical. Therefore, powering IoT devices by harvesting energy from ambient sources, such as that available in the RF spectrum, is an attractive solution. The antenna for the RF energy harvester

Towards fractional-order capacitors with broad tunable constant phase angles: Multi-walled carbon nanotube-polymer composite as a case study

1 min read ·
Agamyrat Agambayev, et al., "Towards fractional-order capacitors with broad tunable constant phase angles: Multi-walled carbon nanotube-polymer composite as a case study." Journal of Physics D: Applied Physics 51(6), 2018, 065602. In this study, multi-walled carbon nanotube (MWCNT) filled polyevinelidenefluoride-trifluoroethylene-chlorofluoroethylene composites are used to realize fractional-order capacitors (FOCs). A solution-mixing and drop-casting approach is used to fabricate the composite. Due to the high aspect ratio of MWCNTs, percolation regime starts at a small weight percentage (wt%)

An approximate multiplier based hardware implementation of the izhikevich model

1 min read ·
Hassan, Salma, et al., "An approximate multiplier based hardware implementation of the izhikevich model." IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2018, 492. This paper proposes the use of approximate multipliers in the hardware implementation of Izhikevich spiking neuron model. The accuracy of the model is investigated by calculating various types of errors on a single neuron and this analysis shows that the proposed model follows the original model. It shows that the proposed model reproduces the same firing patterns as the original one. The network

Analog backpropagation learning circuits for memristive crossbar neural networks

1 min read ·
Krestinskaya, Olga, "Analog backpropagation learning circuits for memristive crossbar neural networks." 2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2018, 1. The implementation of backpropagation algorithm using gradient descent operation with analog circuits is an open problem. In this paper, we present the analog learning circuits for realizing backpropagation algorithm for use with neural networks in memristive crossbar arrays. The circuits are simulated in SPICE using TSMC 180nm CMOS process models, and HP memristor models. The gradient descent operations are

Fractional-Order Hartley Oscillator.

1 min read ·
Agambayev, A., et al., "Fractional-Order Hartley Oscillator". PRIME, 2018, 45. A fractional-order capacitor (FOC) is developed using a Molybdenum disulfide ferroelectric polymer composite. The fabricated FOC exhibits constant phase over five decades between 100 Hz-10 MHz, which is the broadest operating frequency bandwidth reported so far for an FOC. Furthermore, a fractional-order Hartley oscillator is built using this FOC, and provide ten times higher oscillation frequency than the frequency of the conventional Hartley oscillator counterpart.

Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits

1 min read ·
Olga Krestinskaya, et al., "Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits" IEEE Transactions on Circuits and Systems-I 66 (2), 2019, 719. The on-chip implementation of learning algorithms would speed up the training of neural networks in crossbar arrays. The circuit level design and implementation of a back-propagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we propose analog backpropagation learning circuits for various memristive learning architectures, such as deep neural

Approximate Computing With Stochastic Transistors’ Voltage Over-Scaling

1 min read ·
Ren Li, et al., "Approximate Computing With Stochastic Transistors’ Voltage Over-Scaling" IEEE Access 7, 2019, 6373. Ubiquitous computing and the ever-rising need for energy efficiency pose challenges in terms of the processing requirements and the corresponding machine complexity. Nonetheless, the nature of the underlying applications, particularly dealing with real-world data, offers alternative paradigms toward the efficient utilization of the available design resources. In this paper, approximate computing is addressed as an accommodating technique that can benefit from the inherent

Low-Power Hardware Implementation of a Support Vector Machine Training and Classification for Neural Seizure Detection

1 min read ·
Heba Elhosary, et al., "Low-Power Hardware Implementation of a Support Vector Machine Training and Classification for Neural Seizure Detection." IEEE Transactions on Biomedical Circuits and Systems 13 (6), 2019, 1324. In this paper, a low power support vector machine (SVM) training, feature extraction, and classification algorithm are hardware implemented in a neural seizure detection application. The training algorithm used is the sequential minimal optimization (SMO) algorithm. The system is implemented on different platforms: such as field programmable gate array (FPGA), Xilinx Virtex-7 and

Scattering Cancellation-based Cloaking for the Maxwell-Cattaneo Heat Waves

1 min read ·
Mohamed Farhat, et al., "Scattering Cancellation-based Cloaking for the Maxwell-Cattaneo Heat Waves". Physical Review Applied, 11(4), 2019, 044089. We theoretically propose scattering cancellation-based cloaks for heat waves that obey the Maxwell-Cattaneo equation. The proposed cloaks possess carefully tailored diffusivity to cancel the dipole scattering from the object that they surround, and thus can render a small object invisible in the near and far fields, as demonstrated by full-wave finite-element simulations. Mantle heat cloaking is further analyzed and proposed to simplify the design

Synthesis and Optimization of Fractional-order Elements Using a Genetic Algorithm

1 min read ·
Aslihan Kartci, et al., "Synthesis and Optimization of Fractional-order Elements Using a Genetic Algorithm". IEEE Access 7, 2019, 80233. This study proposes a new approach for the optimization of phase and magnitude responses of fractional-order capacitive and inductive elements based on the mixed integer-order genetic algorithm (GA), over a bandwidth of four-decade, and operating up to 1 GHz with a low phase error of approximately ±1 ◦ . It provides a phase optimization in the desired bandwidth with minimal branch number and avoids the use of negative component values, and any complex

FPGA Implementation of Dynamically Reconfigurable IoT security Module Using Algorithm Hopping

1 min read ·
Shady Soliman, et al., "FPGA Implementation of Dynamically Reconfigurable IoT security Module Using Algorithm Hopping." Integration 68, 2019, 108. Internet of Things (IoT) is a promising technology that is continuously spreading around the world leading to many challenges facing cryptographic designers who are trying to fulfill the security standards of IoT constrained devices. In this work, a new design is proposed that adds a new dimension of security by using the concept of frequency hopping to generate a pseudo-random pattern for switching between 5 lightweight cryptographic ciphers: AEGIS

An Imperceptible Magnetic Skin

1 min read ·
Abdullah S Almansouri, et al., "An Imperceptible Magnetic Skin." Advanced Materials Technologies 4 (10), 2019, 1900493. Flexible and wearable magnetoelectronics add intriguing new functionalities to natural perception. Of particular interest regarding these artificial skins are wireless sensing and touchless interactions. Biocompatibility and imperceptibility are the most significant features of devices attached to the bodies. A biocompatible magnetic skin is introduced. It offers extreme flexibility, stretchability (>300%), and is lightweight, while maintaining a remanent magnetization up to

An Ultra-Area-Efficient 1024-Point In-Memory FFT Processor

1 min read ·
Hasan E. Yantir, et al., "An Ultra-Area-Efficient 1024-Point In-Memory FFT Processor", Micromachines, 10 (8), 2019, 509. Current computation architectures rely on more processor-centric design principles. On the other hand, the inevitable increase in the amount of data that applications need forces researchers to design novel processor architectures that are more data-centric. By following this principle, this study proposes an area-efficient Fast Fourier Transform (FFT) processor through in-memory computing. The proposed architecture occupies the smallest footprint of around 0.1 mm2 inside

Scattering Theory of Gravity-Flexural Waves of Floating Plates on Water

1 min read ·
Mohamed Farhat, et al., "Scattering Theory of Gravity-Flexural Waves of Floating Plates on Water". arXiv preprint arXiv:1901.09733. Phys. Rev. B 101, 2019, 014307. We combine theories of scattering for linearized water waves and flexural waves in thin plates to characterize and achieve control of water wave scattering using floating plates. This requires manipulating a sixth-order partial differential equation with appropriate boundary conditions of the velocity potential. Making use of multipole expansions, we reduce the scattering problem to a linear algebraic system. The response of a

A Comparison of Artificial Neural Network (ANN) and Support Vector Machine (SVM) Classifiers for Neural Seizure Detection

1 min read ·
Elgammal, M, et al., "A Comparison of Artificial Neural Network (ANN) and Support Vector Machine (SVM) Classifiers for Neural Seizure Detection". IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) 2019, 646. In this paper, two different classifiers are software and hardware implemented for neural seizure detection. The two techniques are support vector machine(SVM) and artificial neural networks(ANN). The two techniques are pretrained on software and only the classifiers are hardware implemented and tested. A comparison of the two techniques is performed on the levels

A Precision, Energy-Efficient, Oversampling, Noise-Shaping Differential SAR Capacitance-to-Digital Converter

1 min read ·
Abdulaziz Alhoshany, et al., "A Precision, Energy-Efficient, Oversampling, Noise-Shaping Differential SAR Capacitance-to-Digital Converter" IEEE Transactions on Instrumentation and Measurement, 68 (2), 2019, 392. This paper introduces an oversampling, noise-shaping differential successive-approximation-register capacitance-to-digital converter (CDC) architecture for interfacing capacitive sensors. The proposed energy-efficient CDC achieves high-precision capacitive resolution by employing oversampling and noise shaping. The switched-capacitor (SC) integrator is inserted between the comparator

Photo-carrier Extraction by Triboelectricity for Carrier Transport Layer-free Photodetectors

1 min read ·
Vincent K. S. Hsiao, et al., "Photo-carrier Extraction by Triboelectricity for Carrier Transport Layer-free Photodetectors." Nano Energy 65, 2019, 103958. Efficient carrier extraction is essential for high performance optoelectronic devices, such as solar cells and photodetectors. Conventional strategies to separate photogenerated carriers typically involve the fabrication of a p-n junction by doping and the use of carrier selective charge transport layers. However, these techniques often require high temperature processes or costly materials. In this work, we demonstrate an innovative and

A Self-Powered Magneto-Acoustic Tracking Transducer

1 min read ·
Almansouri, Abdullah S., et al. "A Self-Powered Magneto-Acoustic Tracking Transducer." (2020). Acoustic telemetry is widely used for many tracking applications. Objects or subjects are tracked by attaching an acoustic transmitter onto them or having them wear them. The acoustic signal is then detected by different microphones to determine the location of the tagged target. The size and the weight of the tag determine the size of the target that can be tracked. In the case of small-sized tags, about 80% of the volume is consumed by the battery and electronics, while less than 20% by the actual

Spectrometer-Free Graphene Plasmonics Based Refractive Index Sensor

1 min read ·
Li Zhang, et al., "Spectrometer-Free Graphene Plasmonics Based Refractive Index Sensor." Sensors 20 (8), 2020, 2347. We propose a spectrometer-free refractive index sensor based on a graphene plasmonic structure. The spectrometer-free feature of the device is realized thanks to the dynamic tunability of graphene’s chemical potential, through electrostatic biasing. The proposed sensor exhibits a 1566 nm/RIU sensitivity, a 250.6 RIU− 1 figure of merit in the optical mode of operation and a 713.2 meV/RIU sensitivity, a 246.8 RIU− 1 figure of merit in the electrical mode of operation. This

Blue Energy Fuels: Converting Ocean Wave Energy to Carbon-based Liquid Fuels via CO2 Reduction

1 min read ·
Siu-Fung Leung, et al., "Blue Energy Fuels: Converting Ocean Wave Energy to Carbon-based Liquid Fuels via CO2 Reduction." Energy & Environmental Science, 2020. Sequestering CO2 in the form of carbon-based liquid fuels would provide both a convenient and sustainable form of energy for practical use as well as mitigate the effects of global warming and climate change. Ocean wave energy is an abundant and relatively stable source of renewable energy, which would be highly desirable for the conversion of CO2 to conveniently stored and transported liquid fuels. In this work, we demonstrate a wave

Scattering theory and cancellation of gravity-flexural waves of floating plates

1 min read ·
Mohamed Farhat, et al., "Scattering theory and cancellation of gravity-flexural waves of floating plates." Physical Review B, 101 (1), 2020, 014307. We combine theories of scattering for linearized water waves and flexural waves in thin elastic plates to characterize and achieve control of water wave scattering using floating plates. This requires manipulating a sixth-order partial differential equation with appropriate boundary conditions of the velocity potential. Making use of multipole expansions, we reduce the scattering problem to a linear algebraic system. The response of a floating

Toward Self-Powered Internet of Underwater Things Devices

1 min read ·
Jose de Oliveira, et al., "Toward Self-Powered Internet of Underwater Things Devices." IEEE Communications Magazine 58 (1), 2020, 68. Exploiting light beams to carry information and deliver power is suggested as a potential technology to recharge batteries in future generations of IoT and IoUT devices while providing optical connectivity. SLIPT has been recently proposed as an efficient method of wireless power transfer between communicating terminals. In this article, we provide an overview of the various SLIPT techniques in the time, power, and space domains. We also demonstrate two SLIPT

A Dual-Mode Nested Rectifier for Ambient Wireless Powering in CMOS Technology

1 min read ·
Abdullah Almansouri, et al., "A Dual-Mode Nested Rectifier for Ambient Wireless Powering in CMOS Technology." IEEE Transactions on Microwave Theory and Techniques, 68 (5) , 2020, 1754. This article proposes (DM) nested radio frequency (RF) rectifier for ambient wireless powering. The proposed architecture utilizes a DM nested feedback circuit to enhance the conductivity of the rectifier at low power while reducing the reverse leakage current at high power by generating supply voltages at the gates of the pMOS rectifying transistors. The proposed rectifier is fabricated in a 65-nm CMOS

Fully Transparent Transceiver Using Single Binary Oxide Thin Film Transistors

1 min read ·
Ali Hassan, et al., "Fully Transparent Transceiver Using Single Binary Oxide Thin Film Transistors." Advanced Electronic Materials 6, 2020, 1901083. Using one binary oxide, a fully transparent thin‐film transistor (TFT)‐based transceiver circuit is presented. The proposed transceiver circuit is fabricated entirely using an atomic layer deposition process. Moreover, the proposed circuit presents two modulation schemes: frequency shift keying and ON/OFF keying. The fabricated TFTs exhibit saturation mobility, threshold voltage, a subthreshold swing, and an ON/OFF ratio of 18.2 cm 2 V− 1 s− 1, 0

Ultra miniaturized InterDigitated electrodes platform for sensing applications

1 min read ·
Z. Wang, et al., "Ultra miniaturized InterDigitated electrodes platform for sensing applications." Microelectronic Engineering 225, 2020, 111253. InterDigitated Electrodes (IDEs) is a generic platform for a wide range of diverse applications with their implementation in sensing modules being a major one. We propose the use of IDCs with deep sub-micron critical dimension; equally spaced electrodes of 200 nm width for enhanced sensing performance and also the method of fabrication thereof. The transducer configuration was studied theoretically with a finite element method simulation by using

EANN: Energy Adaptive Neural Networks

1 min read ·
Salma Hassan, et al., "EANN: Energy Adaptive Neural Networks." Electronics 9 (5), 2020, 746. This paper proposes an Energy Adaptive Feedforward Neural Network (EANN). It uses multiple approximation techniques in the hardware implementation of the neuron unit. The used techniques are precision scaling, approximate multiplier, computation skipping, neuron skipping, activation function approximation and truncated accumulation. The proposed EANN system applies the partial dynamic reconfiguration (PDR) feature supported by the FPGA platform to reconfigure the hardware elements of the neural network

Simplified Modal‐Cancellation Approach for Substrate‐Integrated‐Waveguide Narrow‐Band Filter Design

1 min read ·
Sebastian Celis, et al., "Simplified Modal-Cancellation Approach for Substrate-Integrated-Waveguide Narrow-Band Filter Design". Electronics 2020, 9, 962. Current substrate-integrated-waveguide (SIW) filter design methodologies can be extremely computational and time-inefficient when a narrow-band filter is required. A new approach to designing compact, highly selective narrow-band filters based on smartly positioned obstacles is thus presented here. The proposed modal-cancellation approach is achieved by translating or eliminating undesired modes within the frequency of interest. This is

Organic Field-Effect Transistor-Based Flexible Sensors

1 min read ·
Saravanan Yuvaraja et al., "Organic Field-Effect Transistor-Based Flexible Sensors" Chemical Society Reviews 2020, (Advanced Article). (12) 49, 2020, 3423. Flexible electronic devices have attracted a great deal of attention in recent years due to their flexibility, reduced complexity and lightweight. Such devices can conformably attach themselves to any bendable surface and can possess diverse transduction mechanisms. Consequently, with continued emphasis on innovation and development, major technological breakthroughs have been achieved in this area. This review focuses on the advancements

Towards Efficient Neuromorphic Hardware: Unsupervised Adaptive Neuron Pruning

1 min read ·
Wenzhe Guo, et al., "Towards Efficient Neuromorphic Hardware: Unsupervised Adaptive Neuron Pruning". Electronics 9, 2020, 1059. To solve real-time challenges, neuromorphic systems generally require deep and complex network structures. Thus, it is crucial to search for effective solutions that can reduce network complexity, improve energy efficiency, and maintain high accuracy. To this end, we propose unsupervised pruning strategies that are focused on pruning neurons while training in spiking neural networks (SNNs) by utilizing network dynamics. The importance of neurons is determined by the

Efficient Acceleration of Stencil Applications through In-Memory Computing

1 min read ·
Hasan Yantır, et al., "Efficient Acceleration of Stencil Applications through In-Memory Computing". Micromachines 11, 2020, 622. The traditional computer architectures severely suffer from the bottleneck between the processing elements and memory that is the biggest barrier in front of their scalability. Nevertheless, the amount of data that applications need to process is increasing rapidly, especially after the era of big data and artificial intelligence. This fact forces new constraints in computer architecture design towards more data-centric principles. Therefore, new paradigms such as in

Automating Analogue AI Chip Design with Genetic Search

1 min read ·
Olga Krestinskaya, et al., "Automating Analogue AI Chip Design with Genetic Search" Adv. Intell. Syst, 2000075, 2020, 2. Optimization of analogue neural circuit designs is one of the most challenging, complicated, time‐consuming, and expensive tasks. Design automation of analogue neuromemristive chips is made difficult by the need to design chips at low cost, ease of scaling, high‐energy efficiency, and small on‐chip area. The rapid progress in edge AI computing applications generates high demand for developing smart sensors. The integration of high‐density analogue computing AI chips as

Growth of large-scale MoS2 nanosheets on double layered ZnCo2O4 for real-time in-situ H2S monitoring in live cells

1 min read ·
Mani Veerappan et al., "Growth of large-scale MoS2 nanosheets on double layered ZnCo2O4 for real-time in-situ H2S monitoring in live cells"Journal of Materials Chemistry B, 2020, 1. There is an urgent need to develop in-situ sensors that monitor the continued release of H2S from biological systems to understand H2S-related pathology and pharmacology. For this purpose, we have developed a molybdenum disulfide supported double-layered zinc cobaltite modified carbon cloth electrode (MoS2–ZnCo2O4–ZnCo2O4) based electrocatalytic sensor. The results of our study suggest that the MoS2–ZnCo2O4–ZnCo2O4

Multi‐source ambient energy harvester based on RF and thermal energy: Design, testing, and IoT application

1 min read ·
Azamat Bakytbekov, et al., "Multi‐source ambient energy harvester based on RF and thermal energy: Design, testing, and IoT application." Energy Science & Engineering, 2020,1. Billions of wireless sensing devices must be powered for IoT applications. Collecting energy from the ambient environment to power sensor nodes is a promising solution. Solar energy has been one of the main sources of ambient energy due to its availability, higher power density, and the maturity of the solar photovoltaic industry. However, there are many scenarios (indoor environment, outdoor environment during nighttime

A multi-bit fully integrated thin-film transistor NO2 gas detector at room temperature

1 min read ·
Mani Teja, et al., “A multi-bit fully integrated thin-film transistor NO2 gas detector at room temperature”, IEEE sensor letters 4 (8), 2020, 5501204. Inexpensive and compact gas sensing systems are indispensable for accurate air quality monitoring with broad spatial coverage. We report an InGaZnO thin-film transistor (TFT)-based integrated NO 2 gas detection system that yields a digital output proportional to the NO 2 concentration. This is the first report on integrated TFT-based detectors where gas concentration is quantified and where simultaneous digital output is provided without

Numerical modeling for terahertz testing of non-metallic pipes

1 min read ·
Mohammed Farhat et al., "Numerical modeling for terahertz testing of non-metallic pipes" AIP Advances 10, 2020, 095112. In the oil and gas industry, safety and operational efficiency at production sites are of paramount importance. A reliable non-destructive testing technology for non-metallic pipes has a high potential financial impact, since it may facilitate the replacement of metallic pipes with non-metallic ones. This article features a perspective and future trends in the field of terahertz sensing technology. Importantly, several numerical simulations that illustrate many exciting

Polarization Insensitive and Transparent Frequency Selective Surface for Dual Band GSM Shielding

1 min read ·
Yiming Yang, et al., "Polarization Insensitive and Transparent Frequency Selective Surface for Dual Band GSM Shielding." IEEE Transactions on Antennas and Propagation, 2020 This paper proposes a single-layer, dual-band, optically transparent frequency selective surface (FSS) for GSM shielding. The design evolves from a fractal cross dipole to achieve dual band response, polarization insensitivity and wide angle of incidence stability with a miniaturized size. A two-axis symmetric structure helps to provide a stable frequency response for incident waves with different polarization angles up to

Rail-to-rail complementary input StrongARM comparator for low-power applications

1 min read ·
Mohammed Al Qadasi., et al. "Rail-to-rail complementary input StrongARM comparator for low-power applications." IET Circuits, Devices & Systems, 2020. This study proposes a new scheme for rail-to-rail input StrongARM latch comparator. Additional differential input p-type metal-oxide-semiconductor (PMOS) and cross-coupled n-type metal-oxide-semiconductor (NMOS) transistors have been introduced to achieve the rail-to-rail input range. The proposed scheme offers low-energy consumption of 15.2 fJ and a high speed of 3 GHz, which makes it attractive for energy harvested Internet of Thing

Unsupervised adaptive weight pruning for energy-efficient neuromorphic systems Frontiers in Neuroscience, section Neuromorphic Engineering.

1 min read ·
Wenzhe Guo, et al., "Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems." Frontiers in Neuroscience 14, 2020, 1189. To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high energy consumption in neuromorphic hardware systems. In this work, we propose an unsupervised online adaptive weight pruning method that dynamically removes non-critical weights from a spiking neural network (SNN) to reduce network complexity and improve
<> Deep learning (140)

Synergizing Nanophotonics and Artificial Intelligence: From Inverse Design to Intelligent Optical Frontends

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B3 R5220

This dissertation investigates the transformative convergence of nanophotonics and artificial intelligence (AI).

Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

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B5 R5209

This dissertation develops integrated spatio-temporal forecasting approaches that combine deep learning, climate data, spatial dependencies, and human mobility to improve dengue prediction and support early-warning systems in Brazil.

Rigorous Model-Constrained Scientific Machine Learning for Digital Twins: A Computational Mathematics Perspective

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B1 L3 R3119

This talk will outline a principled pathway from traditional computational mathematics to rigorously grounded Scientific Machine Learning (SciML) and present recent Scientific Deep Learning (SciDL) methods for forward modeling, inverse and calibration problems, and uncertainty quantification, emphasizing mathematical structure, stability, and generalization.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations
Visiting student at King Abdullah University of Science and Technology (KAUST).

Thoughts About Machine Learning

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B9, Lecture Hall 1, R-2322

A weekly seminar series from January 26 to April 20, 2026, exploring advanced AI concepts beyond the scope of standard deep learning courses.

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Zakaria El Kassimi

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Observer-Relative Flow Visualization and Objective Feature Extraction

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B1 L4 R4214

This dissertation develops an integrated toolkit of novel visualization and feature extraction methods, grounded in a Riemannian geometry framework, to enable an objective, observer-relative, and physically consistent analysis of complex unsteady flows.

Training Neural Networks at Any Scale

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B9 L2 R2325

The talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning, spanning domains from vision to language models.

Deep learning accelerates research on early pregnancies

1 min read ·
KAUST researchers have created deepBlastoid, a deep learning tool that surpasses scientists at evaluating laboratory models of the human embryo before pregnancy

Adonai Vera

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Discovery of Low-Dimensional Generative Models for Complex Dynamical Systems

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This thesis presents a data-driven framework for discovering low-dimensional generative models of complex systems by using a library of normal-form equations to identify both observable dynamics and hidden control variables directly from time-series data.

Juan Pablo Muñoz Díaz

FMCW Radar Applications for Automotive and Biomedical Applications

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B1 L3 R3119

This dissertation enhances Frequency Modulated Continuous Wave (FMCW) radar effectiveness for automotive forward-looking Synthetic Aperture Radar (SAR) imaging and biomedical vital sign and dehydration monitoring by developing novel methodologies based on advanced signal processing, deep learning, and MIMO radar techniques.

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Mhd. Emad Alolaby

Sixiu Liu

Ph.D. degree in Earth science and engineering with the King Abdullah University of Science and Technology (KAUST)

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Advanced Breach and Attack Simulation using ML

Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these

Xiang Chen

Ph.D. Student (former), Statistics
Xiang Chen's research focuses on spatio-temporal modeling of climate-sensitive infectious diseases, integrating large-scale health, climate, and human mobility data to improve epidemiological forecasting and support public health decision-making.

Mohammed S. Alharbi

Jinchao Xu

Professor Xu is widely recognized as a leading figure in developing, designing and analyzing fast methods for finite element discretization and large-scale equation solutions.

Israel Jesus Santos Filho

Israel's research is focusing on Pattern Recognition and Digital Signal Processing. He is passionate about the scientific computing ecosystem, with a particular focus on Data Science, Data Engineering, Applied and Computational Mathematics, Pattern Recognition, and Signal Processing.

Ilies Benhabbour

Ph.D. Student, Computer Science
Ilies' research focuses on fingerprinting of intra network semi active components in end-to-end communications.

Carlos Hinojosa

Education and Early Career o Bachelor of System Engineering and Informatics, University: Universidad Industrial de Santander, 2011-2015. o Master of Science in Computer and System Engineering, University: Universidad Industrial de Santander, 2016-2018. o PhD in Computer Science, University: Universidad Industrial de Santander, 2017-2022.

Anum Ali

Intern (former), Information Science Lab
Research Engineer KAUST (King Abdullah University of Science and Technology).Senior engineer at Samsung Research America.
<> sensors (120)

Wedyan Babatain returns to KAUST to establish the Integrated Interfaces Lab

3 min read ·
KAUST alumna Wedyan Babatain returns as the first Saudi female faculty member in the Electrical and Computer Engineering program, launching a new lab focused on adaptive electronic interfaces and systems.

Tiny sensor could transform head injury detection

1 min read ·
After losing his brother to undetected head trauma, a KAUST researcher helped design a sensor that instantly distinguishes minor bumps from dangerous blows.

Saudi smarter cities start with KAUST

1 min read ·
A KAUST-developed system offers scalable, real-time data to address urban air pollution

Sensing stress to keep plants safe

1 min read ·
A minimally invasive sensor can continuously measure levels of stress hormones.
Zinan Xiao profile picture

Zinan Xiao

Ph.D. Student, Photonics Laboratory
Education Aug 2024 - present, Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Oct 2020 - Oct 2021, M.S. in Optics and photonics at Imperial College London, London, UK. Sep 2016 - Jun 2020, B.S. in Optoelectronic information engineering at Chongqing University, Chongqing, China.

New tech detects dehydration by touching a screen

1 min read ·
A smartphone-style sensor reads moisture levels in skin, offering a low-cost way to monitor health during fasting, exercise or everyday life.

FMCW Radar Applications for Automotive and Biomedical Applications

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B1 L3 R3119

This dissertation enhances Frequency Modulated Continuous Wave (FMCW) radar effectiveness for automotive forward-looking Synthetic Aperture Radar (SAR) imaging and biomedical vital sign and dehydration monitoring by developing novel methodologies based on advanced signal processing, deep learning, and MIMO radar techniques.
Olga Krestinskaya

International recognition for KAUST Ph.D. candidate Olga Krestinskaya

3 min read ·
Olga Krestinskaya, a KAUST Ph.D. student, wins Best Poster at the 2024 Nature Neuromorphic Computing Conference for her work on energy-efficient AI hardware.

Implantable and Wearable Microelectronic Devices to Improve Quality of Life for People with Disabilities

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B3 L5 R5209

Implantable medical devices (IMD) and neuroprostheses are finding applications in new therapies thanks to advancements in microelectronics, sensors, RF communications, and medicine, which have resulted in embedding more functions in IMDs that occupy smaller spaces down to millimeters and consume less power, while offering therapies for more complex diseases and disabilities. I will address the latest developments in key building blocks for state-of-the-art IMDs.

Design and Optimisation of Next generation Compact Photonic Sensors

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B3 L5 R5209

Although optical sensors incorporating grating inscribed and etched fibres are now sufficiently mature and well established in the market, however, designs based on more exotic nanowires and photonic crystal fibres are becoming increasingly important and showing much improved sensitivity by accessing a larger evanescent field. Similarly, novel planar design concepts, such as the silicon slot guide-based design is showing even greater promise, allowing the exploitation of well-developed CMOS fabrication technologies for potentially low-cost sensor elements.

Flexible, Wearable, Disposable Wireless Communication and Sensing Systems Through Additive Manufacturing - 2019

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B9 L2 H1

With the advent of wearable sensors and internet of things (IoT), there is a new focus on electronics which can be bent so that they can be worn or mounted on non-planar objects. Due to large volume (billions of devices), there is a requirement that the cost is extremely low, to the extent that they become disposable. The flexible and low-cost aspects can be addressed through additive manufacturing technologies such as inkjet, screen and 3D printing. This talk introduces additive manufacturing as an emerging technique to realize low cost, flexible and wearable wireless communication and sensing systems.

Integration Strategy for Standalone Compliant Interactive Systems for Add-on Electronics

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B9 L3 R3125

Physically compliant electronics are scientifically intriguing, mechanically complex, ‎technologically ‎challenging but with huge socio-economical potential. Until now the target applications ‎for ‎flexible electronics have been limited to displays, solar cells, printed batteries, wearables, and ‎implantable. However, with the emergence and growth of Internet of Things (IoT) devices ‎worldwide ‎from nearly 27 billion in 2017 to 125 billion in 2030, in this PhD research, expanding the ‎horizon of ‎applications for flexible electronics toward existing “things” will be explored. Low-cost sustainable materials as active electronic materials and a ‎Do-It-Yourself (DIY) integration strategy is used to build “Add-on” standalone sensory system which can be ‎attached to any ‎existing things like a decal. Such electronics can also be embedded in newly minted ‎devices specially ‎using additive methods.

Liquid Metal-Based Inertial Sensors for Motion Monitoring and Human Machine Interfaces

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B2 B3 A0215

In this dissertation, the development of novel practical resistive and capacitive-type inertial sensors using liquid metal as a functional proof mass material is presented. Utilizing the unique electromechanical properties of liquid metal, the novel inertial sensor design confines a graphene-coated liquid metal droplet inside tubular and 3D architectures, enabling motion sensing in single and multiple directions. Combining the graphene-coated liquid metal droplet with printed sensing elements offers a robust fatigue-free alternative material for rigid, proof mass-based accelerometers. Resistive and capacitive sensing mechanisms were both developed, characterized, and evaluated. Emerging rapid fabrication technologies such as direct laser writing and 3D printing were mainly adopted, offering a scalable fabrication strategy independent of advanced microfabrication facilities. The developed inertial sensor was integrated with a programmable system on a chip (PSoC) to function as a stand-alone system and demonstrate its application for real-time- monitoring of human health/ physical activity and for soft human-machine interfaces. The developed inertial sensor architecture and materials in this work offer a new paradigm for manufacturing these widely used sensors that have the potential to complement the performance of their silicon counterparts and extend their applications.

Prof. Atif Shamim Elevated to Senior Member of IEEE

1 min read ·
Prof. Atif Shamim has been elevated to the rank of Senior Member of IEEE. This is considered an important honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

Dr. Atif Shamim appointed Associate Editor for IEEE Transactions on Antennas & propagation

1 min read ·
Dr. Atif Shamim has been appointed Associate Editor for IEEE Transactions on Antennas & Propagation (TAP). He has been included in the editorial board due to his work on "Co-design of on-chip antennas and circuits on CMOS platform".

5.2-GHz RF Power Harvesting Module in 0.18 μm CMOS for Biomedical Implantable Sensors

1 min read ·
Mahmoud H. Ouda, et al., "5.2-GHz RF Power Harvesting Module in 0.18 μm CMOS for Biomedical Implantable Sensors" IEEE Transactions on Microwave Theory and Techniques 61, (5), 2013, 2177. Abstract: A first fully integrated 5.2-GHz CMOS-based RF power harvester with an on-chip antenna is presented in this paper. The design is optimized for sensors implanted inside the eye to wirelessly monitor the intraocular pressure of glaucoma patients. It includes a five-stage RF rectifier with an on-chip antenna, a dc voltage limiter, two voltage sensors, a low dropout voltage regulator, and MOSCAP based on

Prof. Khaled Salama appointed Associate Editor for IEEE Transactions on Circuits and Systems II: Express Briefs

1 min read ·
Prof. Khaled N. Salama was appointed Associated Editor for IEEE Transactions on circuits and systems-II: Express Briefs (TCAS II) for the period 2014-2015.

Fahad's paper got accepted in the IEEE Transactions on Antennas and Propagation

1 min read ·
Muhammad Fahad Farooqui's paper on inkjet printed flood sensor has been accepted for publication at the IEEE transactions on antennas and propagation​​.

Prof. Khaled Salama's Work On Fractional Order Element Based Impedance Matching, Granted US Patent US 8762114

2 min read ·
Prof. Khaled Salama in collaboration with Dr. Ahmed Radwan and Prof. Atif Shamim obtained a US Patent

Muhammad Fahad Farooqui and Ahmed Nafe's paper selected as best paper

1 min read ·
Paper by KAUST students Muhammad Fahad Farooqui and Ahmed Nafe from Prof. Atif Shamim's group has been selected as the best paper in the "Inkjet-Printed Components category".

Professor Jurgen Kosel Elevated To Senior Member Of IEEE

1 min read ·
Prof. Jurgen Kosel has been elevated to the rank of Senior Member of IEEE. This is considered an important honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

A novel design of antenna array developed by KAUST scientists

2 min read ·
At the sensors lab, Prof. K.N. Salama and Prof. A. Shamim led a team of KAUST founding masters students Farhan Abdul Ghaffar and Muhammad Umair Khalid to develop an aperture coupled patch antenna array SoP.

PhD Students Invited to 65th Lindau Nobel Laureate Meeting

1 min read ·
Two CEMSE doctoral students have been invited to the 65th Lindau Nobel Laureate Meeting, which will be held on June 28-July 3.

An origami 'slinky' that harvests incidental energy

1 min read ·
Lightweight and low-cost device uses friction inside paper-based coils to transform mechanical motion into electricity.

KAUST Researchers Offer 'Touch' To The World

1 min read ·
We proudly announced that he had won the award for the "Best Research Paper" at the 2015 IEEE Sensors Applications Symposium for his research on artificial skin.

CEMSE Bridging science: IEEE-NEMS best paper award bestowed to CEMSE for scientific boundaries crossing

1 min read ·
Scientific boundary-crossing initiatives are what help scientists stepping across boundary lines that traditionally mark the divide between scientific disciplines.

This is your light speaking, how can I help you?

1 min read ·
Prof. Salama's Smart Lighting System won the 1st prize at WEP Science Fair with a device that will change the way we interact with light.

Making Artificial Skin inspired from nature

1 min read ·
Congratulations to Professor Jurgen Kosel for his research on an Artificial Skin inspired by nature. Research that was featured in MIT Technology Review.

Better routing for wireless networks

2 min read ·
An improved wireless communication protocol enables more robust links between remote electronic devices.

New low cost diagnostic for cardiovascular risk detection

1 min read ·
A new flexible, low-cost cardiovascular risk marker biosensor was developed by Prof. Khaled Salama and his team at KAUST University, Saudi Arabia.

Miniature flexible sensor to detect heart disease

1 min read ·
A new biosensor made of laser-etched electrodes on a gold-coated polymer may provide an effective and cost-efficient way to assess heart disease risk.

PhD student Ahmed Alfadhel interviewed in New Scientist weekly

1 min read ·
In an interview with New Scientist weekly, PhD student Ahmed Alfadhel from Prof. Jurgen Kosel's team spoke about his research on bio-inspired artificial skin.

Published paper: Single readout technique published at Nature Scientific Reports

1 min read ·
Prof. Khaled Salama and his team managed to achieve the theoretical limit of a single memory access per pixel for a gateless memristor array readout at a fraction of the power of state-of-the-art readout techniques.

Akram's paper in Advanced Materials journal

1 min read ·
Akram's work in collaboration with Integrated Nanotechnology Lab published in the inaugural issue of Advanced Material Technologies

100 % acceptance of IMPACT Lab at the Premier Microwave Conference IMS

1 min read ·
All four submissions from IMPACT Lab in the leading conference of the microwave community IMS 2016 have been accepted.​

Jurgen Kosel delivers seminar at Berkeley Sensor and Actuator Center

1 min read ·
The Berkeley Sensor & Actuator Center (BSAC), founded in 1986 as the National Science Foundation Industry/University Cooperative Research Center for Microsensors and Microactuators. Professor Kosel spoke about magnetic micro- and nanosystems research being conducted at the Sensing, Magnetism, and Microsystems group at KAUST.

Professor Khaled Salama awarded US patent on MEMS fractal capacitors

1 min read ·
Patent application US 20140239446 A1 on "Fractal Structures For Fixed MEMS Capacitors" was awarded a USA patent.

Three new patents for Professor Khaled Salama and the Sensors Lab Team

1 min read ·
With the latest project on Digital Chaos Systems, KAUST's Associate Professor of Electrical Engineering Khaled Salama and the Sensors-Lab's team (http://sensors.kaust.edu.sa/research/current/digital-chaos-systems) secured stunning achievements and three key patents, opening new frontiers for cybersecurity, cryptography, and electronic devices.

Prof. Salama's paper hits the top for 6 months in the row!

2 min read ·
In August 2016, Prof. Salama's article "Stochasticity Modeling in Memristors", was for the sixth time consecutively among the most downloaded paper of the month and the most read article in the IEEE Transactions on Nanotechnology (TNANO).

Flash Microfluidics, rapid Lab on a Chip fabrication method for cost- effective in situ production.

2 min read ·
From DNA sequencing to environment monitoring, microfluidics devices have been broadly employed, but their production is more and more time-consuming and expensive. Researchers from the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, have developed an innovative prototyping method for Lab on a Chip (LOC) production to plummet costs while augmenting performances.

Ph.D. student Muhammad Akram Karimimi among Best Paper Award finalists

1 min read ·
Ph.D. student Muhammad Akram Karimimi from the research team of Atif Shamim, Assistant Professor of Electrical Engineering at KAUST and Director of IMPACT Laboratory, was one of the finalists in the Student Paper Competition at the International Microwave Symposium held in San Francisco, California, in May 2016.

Professor Shamim's Smart Bandage hits Nature Scientific Report and win the IEEE MECAP' 16 Best Paper Award

1 min read ·
Professor Shamim's new cutting edge, flexible and low-cost technology to monitor chronic wounds recently put CEMSE on the map of health care system research. His scientific paper " Low-Cost Inkjet Printed Smart Bandage for Wireless Monitoring of Chronic Wounds " appeared on Nature Scientific Reports and won IEEE MECAP'16 - Middle East Conference on Antennas and Propagation - Best Paper Award, in Beirut, last September.

Gentle sensors for diagnosing brain disorders

1 min read ·
New sensor design paves the way for safer and more effective brain monitoring.

Keeping pollution under control

1 min read ·
Sophisticated statistical methods can detect ozone pollution hot spots and monitor instrument failure.

KAUST-born robotic avatar sets new era for undersea archaeology

2 min read ·
In 2015, an extensive archaeological programme led by the French Ministry of Culture and the University of Montpellier set up a scientific collaboration with Khaled Nabil Salama, Professor from CEMSE Division and co-worker Professor Christian R. Voolstra, from the KAUST Red Sea Research Center, in partnership with Stanford University and Meka Robotics, in California, to develop Ocean One, an agile avatar that affords immediate and haptic-visual interaction – the process of recognizing objects through touch - in the ocean environment at depths greater than 50m.

Rolling out an e-sticker revolution

1 min read ·
High-speed fabrication developed at KAUST can turn out adhesive and flexible electronic devices in any shape imaginable.

TAQADAM Grant from Sabb Bank

1 min read ·
​Prof. Ghanem was awarded a TAQADAM grant from Sabb Bank for a project that focuses on developing AR on wearable devices for the sensory impaired.

Wrist sensor for health screening

2 min read ·
Give a Post-It Note paper to an Electrical Engineering team and you will see it turn onto the new alley to revolutionize modern medicine; an unprecedented wearable, budget-friendly and reliable a paper-based sensor for health screening encapsulated in a silicon wrist with a recycling high-tech design. Body temperature, sweat levels, heart rate and blood pressure can now be monitored with no need to go to hospitals nor using costly technologies, dramatically rare in underdeveloped or developing countries.

Feeling the heat in cells

1 min read ·
New nano-sized sensors provide unprecedented data on how heat diffuses in and out of living cells.

The next generation of sensing platforms

3 min read ·
The University welcomed 70 delegates to the third annual KAUST Sensor Initiative, held on campus from May 7 to 8. The interdisciplinary meeting aimed to transform sensor technologies and explore novel applications, create a global group of sensor pioneers and mentor a new generation of sensor experts.

Fahad's environmental sensor node work is highlighted on KAUST Discovery!​​​

1 min read ·
Fahad's paper ' 3D printed disposable wireless sensor with integrated microelectronics for large area environmental monitoring.' is on the KAUST Discovery page.

Akram presented a poster and gave a live demo in IEEE Sensors Conference

1 min read ·
Muhammad Akram Karimi, Ph.D. student in IMPACT Lab, presented a poster titled "A wearable 3D motion-sensing system integrated with a Bluetooth smartphone application-A system-level overview" at IEEE Sensors conference held in Glasgow, Scotland on Nov 1, 2017.

UESTC students visit CEMSE Laboratories during a one week Winter Camp at KAUST

1 min read ·
Twenty-two undergraduate students from the University of Electronic Science and Technology of China (UESTC), Chengdu, are currently visiting the CEMSE Division as part of one week Winter Camp at KAUST.

Developing disposable lifesaving sensors

1 min read ·
Smart Disposable Sensor Work Story on KAUST Main Page.

The raw power of human motion

1 min read ·
Standalone power modules that harvest and convert vibrations from their surroundings into electricity could soon fuel future microsystems.

Vaseem's Magnetic work highlighted on KAUST Discovery

1 min read ·
Vaseem's Magnetic work highlighted on KAUST Discovery and also got several media attention in technology websites!

Lining MOF pockets to detect noxious gases

1 min read ·
A porous material with tailor-made pockets stitched into its structure is a promising material for sensing noxious gases. A thin film of the material, coated onto an electrode, formed an electronic sensor that could detect traces of sulfur dioxide gas. The sensor is a significant step toward real-world devices that can sniff out dangerous gases in real air. Although several lab-based analytical instruments can detect traces of a specific gas in the air, these instruments are typically large, expensive, power-hungry machines. There is still a need for small, inexpensive, energy-efficient

Somayah Albaradei wins at City of the Future competition

1 min read ·
​CBRC Ph.D. student Somayah Albaradei won first place in the People's Choice category of the 2018 Spring Enrichment Program's ePoster-City of the Future competition. She won an iPad Pro and received a certificate of recognition.

Hussain is taking a flexible approach

1 min read ·
“The theme of our research is how we can empower humanity with technology,” said Muhammad Mustafa Hussain, professor of electrical engineering at KAUST. Using the skills from his time in industry, and the support he is granted by KAUST, Hussain creates flexible, wireless electronics to inexpensively turn everyday objects into smart devices. When Hussain had his novel computer chip technology licensed by Intel Corporation before he had even graduated from his Ph.D., he soon found himself a rewarding job in the semiconductor chip industry. But Hussain harbored the desire to one day go into

The internet of sea things

1 min read ·
When Atif Shamim, KAUST associate professor of electrical engineering, was approached by colleagues in the University's Red Sea Research Center (RSRC) with the challenge of developing underwater sensing technology for marine animals, he wasn't sure how his expertise could help, since marine animals are under water most of the time. Up until now, his research has focused on Bluetooth for applications on dry land, such as wearable medical devices connected to smartphone applications through Bluetooth communication protocol

KAUST partners with McLaren Racing on R&D

1 min read ·
McLaren Racing and KAUST signed a five-year research and development (R&D) agreement focused on extreme performance technology. The partnership aims to advance research in the areas of computational fluid dynamics (CFD), machine learning, fuels and lubricants, advanced mathematics and sensors and electronics. "This partnership underlines our commitment to supporting global STEM advancements," said Jonathan Neale, chief operations officer of McLaren Group. "KAUST is a leading research and education institution in these fields and mirrors our core values. McLaren Racing is part of a world

Paper sensors remove the sting of diabetic testing

1 min read ·
Inkjet-printed device helps monitor a patient’s blood sugar levels without painful needles.

Prof. Shamim gave opening Plenary talk at ACES 2019 (Miami)

1 min read ·
​Prof. Atif Shamim was invited as a plenary speaker for Applied Computational Electromagnetics Symposium (ACES) 2019 held in Miami, Fl, USA.

3D-printed cube dials into energy harvesting

1 min read ·
Ambient energy emitted by cellular phones and modems can be captured and converted into electricity using unusually shaped technology.

Tutku's first paper accepted at Sensors And Actuators B: chemical

1 min read ·
The present study describes the one-step synthesis of NiO nanosheets and successful surface modification of NiO nanosheets with Au NPs. This surface modification step is vital for an application (i.e., hydrazine sensing), where Au NPs enhance the surface area and provide synergistic effect during the electrocatalytic reaction. Better sensing performance, good reproducibility, cyclic stability, and selectivity confirms the reliable and promising application for hydrazine detection. The excellent electrocatalytic reaction for hydrazine demonstrates that these Au NPs modified NiO nanosheets can

Marine sensor gets to grips with salt

1 min read ·
Laser-induced graphene formation creates tailor-made sensors for monitoring ocean creatures and ecosystems.

Prof. Shamim was invited to attend IEEE - AP-S Workshop in Cairo

1 min read ·
Prof. Shamim was invited to attend IEEE - AP-S Workshop Emerging Antennas’ Future Perspectives towards 5G Era in Cairo on Jan 29th, 2019.

​A head impact alert system was designed by four international students from KAUST Microelectronics Winter Camp

1 min read ·
A head impact alert system was designed when we received four international students from different backgrounds this February from the Microelectronics Winter Camp at KAUST.

Student Focus: Muhammad Akram Karimi

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Muhammad Akram Karimi describes himself as an inquisitive-minded researcher who loves to solve industrial challenges through science and technology. Karimi is a third year Ph.D. student working in the Integrated Microwaves Packaging Antennas & Circuits Technology (IMPACT) Lab under the supervision of Professor Atif Shamim. His current research focus is on the design of low-cost and printed microwave sensors for various industrial applications, with a prime focus on the oil industry.

MOFs can sense and sort troublesome gases

1 min read ·
Fluorinated metal-organic frameworks make excellent materials for selective sensing and removal of toxic gases.

Tiny, fast, accurate technology on the radar

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A tiny, portable radar device could allow visually impaired people, or unmanned moving devices, to detect objects in real time.

Putting the sense in materials

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An interdisciplinary initiative is helping KAUST be at the forefront of a digital revolution, where sensors can find a use just about anywhere.

Smart pill bottle keeps drugs safe

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Low-cost, stretchy sensors can be assembled inside the lid of a drug container to help monitor patient safety.

KAUST Ph.D. student wins Three Minute Thesis competition

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Muhammad Akram Karimi, a fourth-year KAUST Ph.D. student working in the Integrated Microwaves Packaging Antennas & Circuits Technology (IMPACT) Lab headed by Associate Professor Atif Shamim, won the Three Minute Thesis (3MT) competition held during the IEEE Microwave Theory and Techniques Society's International Microwave Symposium (IMS2019) in Boston, Massachusetts, U.S, in early June. IEEE IMS has been taking place for 60 years, and it is the flagship conference for microwave engineers and scientists.

Democratizing electronics one step at a time

3 min read ·
"You would not believe how many amazingly talented people there are in the world, but they often are just not exposed to opportunities," noted Muhammad Mustafa Hussain, KAUST professor of electrical engineering and currently a visiting professor at the University of California, Berkeley. He is determined to try and change this—one step at a time.

Single-electrode material streamlines functions into a tiny chip

1 min read ·
Ruthenium oxide is used to integrate energy-storing microsupercapacitors and thin-film electronics at the transistor level.

Relishing the ‘McLaren experience’ at the 2019 Formula 1 Bahrain Grand Prix

4 min read ·
Altynay Kaidarova is a Ph.D. student in electrical engineering based in the University's Computer, Electrical and Mathematical Science & Engineering division.

Professor Kosel Appointed as Distinguished Lecturer of the IEEE Sensors Council

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Jürgen Kosel, associate professor of electrical engineering in the University's Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, has been appointed as a Distinguished Lecturer of the IEEE Sensors Council for the period of 2020-2022.

Magnetic skin ensures the force is with you

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A magnetic skin that is safe and comfortable to wear could open the door to a wide range of wireless, remotely controlled applications.

Sensor cube helps keep fish farming afloat

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A self-powered water quality sensor could help fish farmers to monitor pollution in their ponds remotely.

Saudi researcher’s magnetic skin research garners widespread attention

3 min read ·
Imagine if there was a revolutionary wearable technology that could enable the user to open doors or operate machinery with a simple wave of their hand or a mere blink of their eye? What people might not realize is that this contact-free human-machine technology already exists—and it has been developed right here in a laboratory at KAUST.

Sensing a heart attack before it strikes

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Nanomaterial-based electronic device monitors a key heart health biomarker.

MMH Labs named 2020 Edison Awards Gold Winner

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The KAUST MMH Labs were recently named a 2020 Edison Awards Gold Winner in the Consumer Electronics and Information Technology category for their wearable technology tagging prototype, Bluefin.

Exciting New Technology Makes BPA Water Testing More Efficient

3 min read ·
In today’s world, it should come as no surprise that plastic dominates the products that we rely on each and every day. From our technology devices, to our water bottles, plastic is almost always an integral structural component.

Light sensors detect larval pests munching on date palms

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Optical fibers wrapped around date palm trunks could help detect this tree’s most destructive pest early enough to save it.

Early warning sensor sniffs out cities' harmful gas

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An integrated detector device could form the basis of a distributed air-quality sensor network.

A Compact, Passive Frequency-Hopping Harmonic Sensor Based on a Microfluidic Reconfigurable Dual-Band Antenna

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Liang Zhu, et al., "A Compact, Passive Frequency-Hopping Harmonic Sensor Based on a Microfluidic Reconfigurable Dual-Band Antenna," IEEE Sensors Journal, 2020, 1. We propose here a fully-passive wireless liquid sensor using a harmonic transponder, which comprises a dual-band microstrip antenna reconfigured by different types of liquids injected in a fluidic cavity. Different from traditional radio-frequency (RF) backscatter sensors, the proposed harmonic-transponder sensor (or harmonic sensor) receives frequency-hopped RF monotones and backscatters their second harmonics, with the peak

A fresh sense of possibility

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A multisensory graphene-based skin can sense in extreme environments where other sensors cannot be used.

Sensors get a laser shape up

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Laser writing breathes life into high-performance sensing platforms.

KAUST alumni receive SPE 2020 Regional Awards

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KAUST alumni Dr. Chinthaka Gooneratne and Dr. Bodong Li both recently received Society of Petroleum Engineers (SPE) 2020 Regional Awards for the Middle East and North Africa Region. Gooneratne won the Regional Drilling Engineering Award, while Li was awarded the Regional Formation Evaluation Award.

KAUST team power ahead with alternative energy research

2 min read ·
With the continued growth in Internet of Things applications, deployments, and devices, highly connected, sustainable smart cities are no longer mere concepts or fantastical future dreams; many are already active and are expanding apace.

KAUST Ph.D. student wins 2021 IEEE Sensors Council Technical Achievement Award

2 min read ·
Muhammad Akram Karimi, KAUST Ph.D. student and co-founder of the KAUST-supported startup company Saher Flow Solutions, has been selected as the recipient of the 2021 IEEE Sensors Council Technical Achievement Award in the Field of Sensors – Early Career. The award honors an individual with “outstanding technical contributions within the scope of the IEEE Sensors Council, as documented by publications and patents. It is based on the general quality and originality of contributions.”

Perovskites used to make efficient artificial retina

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KAUST researchers have built an artificial electronic retina that mimics the behaviour of the eye’s rod retina cells. Their artificial retina can recognise handwritten numbers.

Smart memory devices that can sense

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In-memory sensing technology will facilitate electronic systems that are faster, smaller and consume less energy.

Ditching sensors for streamlined control of microdevices

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Hossein Fariborzi's team has developed a novel miniature microelectromechanical device that avoids the need to use sensors and has significant potential for use in industrial or medical applications.

KAUST students' research wins first place at IEEE AP-S/URSI 2022

2 min read ·
Hanguang Liao and Yiming Yang from the KAUST IMPACT Lab won first place in the 2022 IEEE AP-S Student Design Contest at the 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (IEEE AP-S/URSI 2022), held in July in Denver.
Montserrat Ramirez de Angel receiving the Best Pitch Award at IEEE 2024 Sensors Conference

Giving voice to the voiceless through assistive technology

2 min read ·
Artificial skin coupled with sensors and machine learning technology allows people with voice disorders to communicate using soundless mouth movements.

Wojoud Almubadal

Wedyan Babatain

Ibn Rushd Assistant Professor, Electrical and Computer Engineering
Professor Babatain’s research focuses on developing soft electronic interfaces through innovations in sensing, actuation, and additive manufacturing for wearable technologies, human-machine interfaces and reconfigurable electronics.

Tutku Beduk

Tutku Beduk is currently a PhD student at the Sensors Lab, in the Material Science and Engineering Department, MSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Tutku Bedük received her bachelor degree from Middle East Technical University in Chemistry with a High Honor Degree, Turkey in 2016. She also competed her MSc degree in Bilkent University in the field of Chemistry, Turkey in 2018. She is currently a PhD candidate in the Sensor Lab Group under the direction of Prof. Khaled Nabil Salama at King Abdullah University of Science and Technology

Talha Ariff

Talha is studying the use of novel fiber-optic distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) as a tool to help monitor and control anthropogenic acoustic pollution in marine environments.

Sherjeel Khan

Sherjeel Khan did his Bachelors in Mechatronics Engineering from National University of Sciences and ‎Technology (NUST) Islamabad, Pakistan in 2013. After graduation, he worked as a Design Engineer in a ‎Pakistan-US based startup Cowlar©. He then availed his Master’s degree in electrical engineering from ‎Georgia Institute of Technology, USA in 2016 under Fulbright Scholarship. He has been a PhD student with Professor Muhammad Mustafa Hussain at KAUST since 2016. His research work is focused on making low-cost eco-friendly sensing solutions for healthcare based applications.

Sandeep Surya

Postdoctoral Research Fellow, Sensors
Dr. Sandeep Surya is currently a Post-Doctoral fellow at the Sensors Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Sandeep G. Surya received his Ph.D. degree from IIT Bombay in 2017. He is currently a Post-Doctoral Fellow with Sensors Lab, CEMSE Division, King Abdullah University of Science and Technology. Prior to this, he was a Research Associate with the Microsystems Technology Research Unit, Fondazione Bruno Kessler, Trento, Italy. His forte is in building platforms for different sensor

Mohammad Vaseem

Dr. Mohammad Vaseem is a Research Scientist and Lab Manager in the Integrated Microwaves Packaging Antennas and Circuits Technology (IMPACT) Research Group at King Abdullah University of Science and Technology (KAUST) led by Professor Atif Shamim. Research Interests Mohammad's research interests include Tuning Ink chemistry/Ink-formulation based on metal, metal oxide and dielectric materials, Inkjet-Printed devices supported on paper and plastic electronics, Printed transistors, conductors, RFID, and sensors. Book Chapters: • M. Vaseem, Ahmad Umar and Yoon-Bong Hahn, “Zinc Oxide Nanoparticles

Lana Joharji

Lana Joharji completed her master’s degree at the SAMA Labs in the Electrical and Computer Engineering program at KAUST. She received her bachelor's degree in Electrical and Computer Engineering from King Abdulaziz University (KAU), KSA, in 2021. Her research interests include sensors, actuators, flexible and soft electronics for environmental applications. She received the KAUST Graduate Fellowship for her MS/Ph.D. program. She is a student member of the IEEE and Saudi Council of Engineers. After graduating from the SAMA labs, she joined Tufts University, USA, as a PhD student.

Khaled Salama

Professor Salama is a prominent leader in the development of intelligent sensors, biosensors and very large-scale integration (VLSI) architectures for bioimaging and instrumentation. He is one of the founders of the Electrical and Computer Engineering (ECE) Program at KAUST.

Hommood Alrowais

Hommood Alrowais is a Ph.D. student based in the department of electrical and computer engineering at the Georgia Institute of Technology (Georgia Tech) in Atlanta, Georgia, U.S. Alrowais graduated from KAUST in 2010 as part of the University's first graduating class with a master's degree in electrical engineering. His current research is focused on bio-inspired sensors. "The sensor I am developing is based on the semicircular canal in the inner ear. This tiny organ is responsible for sensing angular rotation in three axes, and it also stabilizes gaze and allows for a smoother reading

Fhad Al-Modaf

Fahad Alam

Postdoctoral Research Fellow, Electrical and Computer Engineering
Dr. Fahad Alam is currently a postdoc at SAMA lab. He received his MSc and MTech from AMU Aligarh, India, in 2009 and 2012, respectively, and PhD from IIT Kanpur, India in 2017. He worked as a Postdoc, in Khalifa University Abu Dhabi from April 2018 to March 2022. He has previously received N-PDF, India, and EU-India (IITK-University of Groningen) exchange fellowship. His research interests include 3D/4D printing and shape memory polymers.

Evangelos Piliouras

Education and early career Evangelos received his integrated master's degree with excellency, from the department of Electrical Engineering and Computer Technology in Patras in 2018. He recently joined King Abdullah University of Science and Technology (KAUST) and he works under the supervision of Prof. Meriem Laleg in the field of estimation. Evangelos did a 3-month internship in Dialog Semiconductor as a software developer, having developed a software automation tool. Research Interests Evangelos' research interests are found in the area of biosensors, embedded system design, modeling and

Christos Sapsanis

Education Profile : - Ph.D. Candidate, Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. - M.Sc., Electrical Engineering (Electrophysics), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2015. - B.Sc., Electrical and Computer Engineering, University of Patras, Greece, 2013. Introduction: Christos Sapsanis obtained his M.Sc. Degree in the Electrical Engineering (EE) Program from King Abdullah University of Science and Technology, under the supervision of Prof. Khaled Nabil Salama. Before joining KAUST, he earned his B.Sc. in Electrical

Chandu V.V. Muralee Gopi

Postdoctoral Research Fellow, Photonics Laboratory
Chandu V.V. Muralee Gopi is a Postdoctoral Fellow at Photonics Laboratory in the Division of Computer, Electrical and Mathematical Science and Engineering (CEMSE) at King Abdullah University of Science and Technology (KAUST). He received his Master's degree in Interdisciplinary Program in Robotics and a Ph.D. degree in Electrical Engineering from Pusan National University, South Korea, in Feb. 2015 and Feb. 2018 respectively. Over the past 6 years, he has held multiple appointments, within South Korea, and has developed broad expertise and a proven track record in the development of highly

Biwei Wang

Visiting Student, Photonics Laboratory
Biwei Wang is a Visiting Student in Photonics Lab at KAUST. He is pursuing a Ph. D degree in The Hong Kong Polytechnic University. Research Interest Biwei's current research focuses on the distributed optical fiber sensors based on Brillouin scattering and the application of machine learning techniques in optical fiber sensor systems. Selected Publications ​ B. Wang, L. Wang, N. Guo, F. N. Khan, A. K. Azad, C. Yu, and C. Lu, “Extraction of temperature distribution using deep neural networks for BOTDA sensing system,” Conference on Lasers and Electro-Optics/Pacific Rim 2017 (CLEO-PR), Paper

Bashayr Alqahtani

Bashayr Alqahtani is an Electrical and Computer Engineering master’s student at King Abdullah University of Science and Technology, and a student at the KAUST Smart, Advanced Memory devices and Applications (SAMA) Laboratory under the supervision of Professor Nazek El-Atab. Her research interests include the fabrication of physics devices such as sensors for industrial and in-memory sensing applications.

Atif Shamim

Professor Shamim is an accomplished electrical engineer specializing in wireless communication and sensing components and systems. His work aims to improve the performance of wireless communication and sensing systems through low-cost additive manufacturing techniques.

Ahmed Nafe

Ahmed Nafe obtained Ms degree in electrical engineering under the supervision of Prof. Atif Shamim from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2014. He received the B.Sc. degree (Hons.) in electrical engineering from the German University in Cairo (GUC), Cairo, in 2012. His B.Sc. thesis concerned the design and characterization of millimeter-wave antenna arrays for automotive radars with Ulm University, Ulm, Germany. He is currently pursuing a Ph.D. degree in electrical and computer engineering at the University of California at San Diego, La Jolla

Aigerim Tankimanova

Visiting Student, Photonics Laboratory
​Aigerim Tankimanova is a Visiting Student in Photonics Lab at KAUST. She is pursuing a Master's degree in Politecnico di Milano in Electronics​ Engineering in Electronics Engineering. She is currently working on her Master Thesis project in KAUST. Research Interests Aigerim Tankimanova's research interests include ​Sensors, Photonics, and Electronic circuits and systems. Education Profile M.Sc. in Electronics Engineering, Politecnico di Milano, Milan, Italy (2017- present). B.Eng. in Electrical and Electronics Engineering, Nazarbayev University, Astana, Kazakhstan (2011-2016).

Ahmed Alfadhel

Ahmed Alfadhel obtained his both MS degree and PhD degree in Electrical Engineering at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Jurgen Kosel. He received a bachelor's degree in electrical engineering from Rochester Institute of Technology, the US in 2010. Ahmed recently co-founded SONATE, a start-up company that manufactures high-quality smart nanomaterials for medical, electronics and energy applications. Research Interests Ahmed's research interests are in the fields of nano-engineering, wearable sensors, and magnetic micro-systems. His

Abdullah Alshehri

My research focuses on designing efficient interfaces between the analog and digital domains through the development of low-power, high-performance CMOS interface circuits. The interface circuit encompasses key components such as amplifiers, filters, comparators, analog-to-digital converters (ADCs), each of which must be carefully optimized to meet the specific requirements of the target application. By addressing the challenges in analog-digital interfacing, this work aims to contribute to the advancement of integrated circuits and systems that can seamlessly connect real-world analog signals to their digital counterparts, with potential applications in sensor interfaces, data acquisition systems, and mixed-signal electronics. The research involves circuit design, simulation, integration, and experimental evaluation to create novel interface circuit topologies and techniques that improve power efficiency, speed, and overall performance.

Abdulaziz Yaslam

Education 2020.08 - Present M.S. Student in Bioengineering, King Abdullah University of Science and Technology, KSA 2016.07 - 2020.06 B.S. in Biomedical Engineering, University of California at Davis, USA Professional Profile 2021.05 - 2021.12 Master's Student Researcher, KAUST RISC Lab, KAUST, KSA 2016.09 - 2018.06 Teaching Assistant, Electrical Installations and Machines, UNAL, Colombia Honors and Awards ​2018.07 "Best Poster" award in the North Carolina State University - KAUST Summer Internship Areas of Expertise and Research Interests Sensors Human-robot interaction Rehabilitation
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Osama N. Hassan

Postdoctoral Research Fellow
Postdoctoral Fellow in the Image and Video Understanding Lab (IVUL) at KAUST, working with Prof. Bernard Ghanem. His research spans computer vision, vision-language and multimodal models, and agentic AI systems, informed by a background ranging from optical sensing and computational imaging to machine learning for perception and AI systems that reason and act.

Paul Gavrikov

Postdoctoral Research Fellow
Paul Gavrikov is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in fundamental deep learning research. His research focuses on interpretability, robustness, generalization, and biases in the vision of multi-modal models.

Modeling physical change for more realistic image editing

2 min read ·
A new KAUST-led approach helps AI understand physical change, improving image edits involving light, motion and materials.

Reem Alzahrani

Ph.D. Student
Reem Alzahrani is an MS/PhD student at KAUST, working on Large Language Models and Vision-Language Models. Her research focuses on identifying and addressing the limitations of AI systems to improve their robustness and reliability.

Ansar Khangeldin

M.S. Student
Ms/PhD Electrical and Computer Engineering student. Have a background in Applied Mathematics, Control Theory, Machine Learning Research and Development, Multi-Agent Reinforcement Learning. Main research interests are application and enhancement of CV methods in autonomous systems (AVs, Robotics, UAVs).

Drishya Karki

Research Engineer
Research Engineer at the KAUST–FIFA Research Institute, working on multimodal sports understanding using video, player positional tracking, and skeletal-joint tracking data. My research focuses primarily on group activity recognition and action spotting.

Demystifying Adversarial Patch Attacks and Defenses in the Physical World

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B9 R2325

In this talk, I will introduce a series of adversarial patch attacks in face recognition systems and autonomous driving cars, and present our recent studies in developing a zero-shot and patch-agnostic defense framework.

Synergizing Nanophotonics and Artificial Intelligence: From Inverse Design to Intelligent Optical Frontends

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B3 R5220

This dissertation investigates the transformative convergence of nanophotonics and artificial intelligence (AI).

KAUST becomes first FIFA research institute in the Middle East and Asia

1 min read ·
KAUST has been designated the first FIFA Research Institute in the Middle East and Asia, and the fifth worldwide, reflecting the University’s commitment to advancing innovation in football through cutting-edge scientific inquiry.

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Overcoming Catastrophic Forgetting: From Efficiency to Safety

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B3 L5 R5220

This thesis addresses the challenge of catastrophic forgetting in AI by developing novel continual learning methods that enhance memory efficiency in video analysis and preserve safety alignment in large language models, ensuring reliable adaptation in both resource-constrained and safety-critical applications.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent
KAUST-CEMSE-CS-VISION-CAIR-Dalal-Aldossary

Dalal E. Aldossary

M.S. Student, Computer Science
Dalal Aldossary is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Deep learning accelerates research on early pregnancies

1 min read ·
KAUST researchers have created deepBlastoid, a deep learning tool that surpasses scientists at evaluating laboratory models of the human embryo before pregnancy

Adonai Vera

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Abdelrahman Eldesokey

Postdoctoral Research Fellow
Abdelrahman Eldesokey is a Postdoctoral Fellow at KAUST, working on Generative AI and Computer Vision. His research focuses on diffusion models, multimodal large language models, and vision foundation models, aiming to make generative systems more controllable, interpretable, and reliable.

Najmeddine Dhieb

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Aznaur Aliev

M.S. Student

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

Ph.D. Student, Computer Science
Ahmad Sait is a PhD student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Master's from KAUST and Bachelor's from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Sim-to-Real Transfer for Autonomous Navigation

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B2 L5 R5220

This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator Sim4CV which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning.

Efficient Localization of Human Actions and Moments in Videos

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B3 L5 R5220

Abstract We are stumbling across a video tsunami flooding our communication channels. The ubiquity of digital cameras and social networks has increased the amount of visual media content generated and shared by people, in particular videos. Cisco reports that 82% of the internet traffic would be in the form of videos by 2022. The computer vision community has embraced this challenge by offering the first building blocks to translate the visual data in segmented video clips into semantic tags. However, users usually require to go beyond tagging at the video level. For example, someone may want

An Egocentric Computer Vision-based Robotic Wheelchair

Mohammed Kutbi, Assistant Professor at the department of Computer Science and a member of the Artificial Intelligence Unit at Saudi Electronic University (SEU)

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B3 L5 R5209

The emerging need to improve the quality of life for elderly and disabled individuals who rely on wheelchairs for mobility is our motivation for this work. Research on robotics wheelchair covers broad range from motion control, how to control the wheelchair movement, to complete autonomy.

Understanding a Block of Layers in Deep Neural Networks: Optimization, Probabilistic and Tropical Geometric Perspectives

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KAUST

In this dissertation, we aim at theoretically studying and analyzing deep learning models. Since deep models substantially vary in their shapes and sizes, in this dissertation, we restrict our work to a single fundamental block of layers that is common in almost all architectures. The block of layers of interest is the composition of an affine layer followed by a nonlinear activation function and then lastly followed by another affine layer. We study this block of layers from three different perspectives. (i) An Optimization Perspective. We try addressing the following question: Is it possible that the output of the forward pass through the block of layers highlighted above is an optimal solution to a certain convex optimization problem? As a result, we show an equivalency between the forward pass through this block of layers and a single iteration of certain types of deterministic and stochastic algorithms solving a particular class of tensor formulated convex optimization problems.

Indoor 3D Scene Understanding Using Depth Sensors

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KAUST

One of the main goals in computer vision is to achieve a human-like understanding of images. This understanding has been recently represented in various forms, including image classification, object detection, semantic segmentation, among many others. Nevertheless, image understanding has been mainly studied in the 2D image frame, so more information is needed to relate them to the 3D world. With the emergence of 3D sensors (e.g. the Microsoft Kinect), which provide depth along with color information, the task of propagating 2D knowledge into 3D becomes more attainable and enables interaction between a machine (e.g. robot) and its environment. This dissertation focuses on three aspects of indoor 3D scene understanding: (1) 2D-driven 3D object detection for single frame scenes with inherent 2D information, (2) 3D object instance segmentation for 3D reconstructed scenes, and (3) using room and floor orientation for automatic labeling of indoor scenes that could be used for self-supervised object segmentation. These methods allow capturing of physical extents of 3D objects, such as their sizes and actual locations within a scene.

Stylistic and spatial disentanglement in GANs

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KAUST

This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

Towards Detailed Understanding of the Visual World

Fahad Khan, Associate Professor at MBZUAI and Linköping University

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B9 L2 R2322

Machine perception that corresponds to the ability to understand the visual world based on the input from sensors, such as cameras is one of the central problems in Artificial Intelligence. To this end, recent years have witnessed tremendous progress in various instance-level recognition tasks having real-world applications in e.g., robotics, autonomous driving and surveillance. In this talk, I will first present our recent results towards understanding state-of-the-art deep learning-based visual recognition networks in terms of their robustness and generalizability. Next, I will present our results on learning visual recognition models with limited human supervision. Finally, I will discuss moving one step further from instance-level recognition to understand visual relationships between object pairs.

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

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B9 L2 R2322 H1

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

Towards Richer Video Representation for Action Understanding

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B2 L5 R5209

With video data dominating the internet traffic, it is crucial to develop automated models that can analyze and understand what humans do in videos. Such models must solve tasks such as action classification, temporal activity localization, spatiotemporal action detection, and video captioning. This dissertation aims to identify the challenges hindering the progress in human action understanding and propose novel solutions to overcome these challenges.

Best Business Plan at VIEW 2016

1 min read ·
Hachid won the award for the Venture pitch with the best business plan at the Fifth Annual Vision Industry and Entrepreneur Workshop (VIEW) 2016.

Fabian and Victor participate in the annual MSR Summer School!

1 min read ·
Two IVUL Ph.D. students (Fabian Caba and Victor Escorcia) participate in the annual MSR Summer School.

Adel Bibi and Matthias Mueller going to ICVSS 2017

1 min read ·
Adel Bibi and Matthias Mueller have been accepted to participate in the ICVSS (International Computer Vision Summer Schoole) 2017 in Italy. Admission into this summer school is quite competitive in the vision community.​​​

CEMSE New Faculty: Mohamed Elhoseiny, Assistant Professor of Computer Science

3 min read ·
Prior to joining the KAUST CEMSE Division earlier this year, Mohamed Elhoseiny received his Ph.D. degree from Rutgers University, New Brunswick in 2016, before spending over two years working as a postdoctoral researcher at Facebook in the company’s AI research wing. Elhoseiny joins the Division as an assistant professor of computer science based in the KAUST Visual Computing Center (VCC). He will also act as the PI of the KAUST Computer Vision, Content AI (Vision-CAIR) Research Group. Outside of his duties at KAUST, he is also acting as an artificial intelligence (AI) research consultant for Baidu Research, Silicon Valley AI Lab.

KAUST Ph.D. student Jinhui Xiong wins best paper award

1 min read ·
KAUST computer science Ph.D. student Jinhui Xiong recently won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization. The symposium took place from September 30 to October 2, 2019, at the University of Rostock, Germany, and provided the opportunity for researchers to discuss a wide range of topics in computer science, including computer graphics, vision, visualization and visual analytics.

Meet KAUST prospective student: Matheus Vinícius Medeiros de Souza

2 min read ·
Matheus Vinícius Medeiros de Souza is a 24-year-old electrical engineering graduate from the city of Natal, Brazil. Matheus first came to KAUST as a visiting student in 2019 and was instantly impressed by the university’s research facilities.

Meet KAUST Prospective Student: Xinge Yang

1 min read ·
Xinge Yang is a 22-year-old graduate who will join KAUST from the University of Science and Technology of China (USTC). Yang will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich.

KAUST prospective student: Qizhou Wang

1 min read ·
Qizhou Wang, 22, is a communication engineering graduate who will join KAUST from the University of Electronic Science and Technology, China. Wang will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate in the KAUST Primalight Lab under the supervision of Professor Andrea Fratalocchi.

KAUST-Stanford neural network paper accepted for presentation at ECCV 2020

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A novel KAUST-Stanford University research paper by Panos Achlioptas (Stanford), Ahmed Abdelreheem (KAUST), Fei Xia (Stanford), Mohamed Elhoseiny (KAUST) and Leonidas Guibas (Stanford) was accepted for presentation at the 16th European Conference on Computer Vision (ECCV 2020).

KAUST prospective student: Xingdi Zhang

1 min read ·
Xingdi Zhang is a computer science graduate from the University of Electronic Science and Technology of China (UESTC), China. Xingdi will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the KAUST High-Performance Visualization Group under the supervision of Professor Markus Hadwiger.

Meet KAUST prospective student: Kilichbek Haydarov

2 min read ·
Kilichbek Haydarov is a graduate student who will join KAUST from Uzbekistan. He will join KAUST as a M.S./Ph.D. candidate and member of the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. Kilichbek obtained his bachelor's degree in computer science and engineering from Sejong University, South Korea.

Meet KAUST prospective student: Kai Yi

1 min read ·
Kai Yi is a graduate from Xi'an Jiaotong University (XJTU), China, who will join KAUST in the fall of 2020. Kai will join KAUST as a M.S./Ph.D. candidate and member of the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. KAUST's high reputation and Kai's interest in computer vision and machine learning prompted him to choose the University to further his academic career.

Prof. Bernard Ghanem to give a talk at Machines Can See 2021

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The fifth annual international summit ‘Machines Can See’ will host IVUL's own Prof. Bernard Ghanem as a speaker. The summit, to be held on July 8th 2021, is hosted by VisionLabs, a leading software company devoted to developing products based on state-of-the-art Computer Vision algorithms. The summit will be a space for Computer Vision and Machine Learning leading experts to discuss technology trends and share experience regarding human-centric technologies. Together with Prof. Bernard Ghanem, the summit will feature Professors Dima Damen, Efstratios Gavves, Ira Kemelmacher-Shlizerman, and

Seven IVUL members recognized as Outstanding Reviewers by CVPR 2021

1 min read ·
The 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) has granted Outstanding Reviewer awards to seven members of IVUL: Alejandro Pardo, Ali Thabet, Chen Zhao, Mattia Soldan, Mengmeng Xu, Juan C. Pérez, and Silvio Giancola. CVPR, a premier annual computer vision event that will take place virtually from June 19th to June 25th, is the largest computer vision conference in the world, gathering researchers from academia and industry alike. The peer-review process through which the conference determines acceptance of papers relies on expert reviewers from across the world

IVUL paper Wins Best Poster award at CVPR Workshop

1 min read ·
A paper by IVUL members won a Best Poster award at the LatinX in Computer Vision Workshop at CVPR 2021, the world-renowned venue for research in Computer Vision. The paper, "BAOD: Budget-Aware Object Detection", was written by four IVUL members, Alejandro Pardo, Mengmeng Frost Xu, Ali K. Thabet, and Bernard Ghanem, in collaboration with Professor Pablo Arbeláez, from Universidad de los Andes. The paper studies the efficiency with which object-detection systems learn when constrained by limited budgets. We congratulate the authors!

Perovskites used to make efficient artificial retina

1 min read ·
KAUST researchers have built an artificial electronic retina that mimics the behaviour of the eye’s rod retina cells. Their artificial retina can recognise handwritten numbers.

Yuanhao Wang

Yuanhao Wang is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Yuanhao Wang obtained his bachelor's degree in Communication Engineering from Beijing University of Posts and Telecommunications in China in 2013. In 2016, he received his master's degree in Integrated Circuits Engineering from Tsinghua University in China. Research Interest Yuanhao’s research interests encompass a wide range of topics in computational photography

Yipeng Qin

Postdoctoral Research Fellow, Computer Science
Yipeng Qin was a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Currently, he is a Lecturer at the School of Computer Science and Informatics in Cardiff University (CU), United Kingdom. Education and Early Career In 2013, Yipeng Qin graduated with a Bachelor of Engineering in Electronic Engineering from Shanghai Jiao Tong University in China. Later during 2017, he obtained his Ph.D. in Computer Science from the National Centre for Computer Animation, Bournemouth University in

Yidan Zheng

Ph.D. Student, Computer Science

Yazeed Alharbi

Yazeed Alharbi is a Ph.D. candidate under the supervision of Prof. Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career In 2015, Yazeed obtained his bachelor degree from Purdue University in the computer graphics and visualization track, with a minor in philosophy. In 2018, he received his master degree from King Abdullah University of Science and Technology (KAUST). He mainly learned about computer vision and the process of publication in that field. Research Interest Currently, Alharbi research is focused

Yahya Bokhari

Postdoctoral Research Fellow, Computer Science
Yahya Bokhari is a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST), working with Professor Wolfgang Heidrich on enhancing X-ray images under Smart Health Initiative. Education and Early Career Yahya Bokhari received his bachelor's degree in Medical Technology from King Abdulaziz University, KSA. He holds a Master's degree in Bioinformatics and a Ph.D. degree in Computer Science from Virginia Commonwealth University, USA. Research Interest Dr. Yahya Bokhari is mainly interested in Image Processing, Computer Vision

Xiang Li

Xiang Li is a Postdoc Researcher at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny in the Vision-CAIR Group. Education and Work Experience Xiang Li joined KAUST as a Postdoc Researcher in Dec 2022. Before that, he was a Postdoc Researcher at the Multimedia and Visual Computing Lab (MMVC) at NYU Tandon and NYU Abu Dhabi from Oct 2019. He received a Bachelor's degree in Remote Sensing Science and Technology from Wuhan University in 2014 and a Ph.D. in Cartography and Geography Information System

Victor Escorcia

Research Scientist committed to innovating AI agents to empower creativity and increase productivity. Passionate about developing cutting-edge AI applications involving video understanding and creative storytelling. I have a wide range of research and engineering interests from on-device AI to General AI. Reach out!

Tarek H. Mahmoud

Areas of Expertise and Research Interests ​Computer Vision Machine Learning Robotics Education Profile ​MS in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2017) ​​BS in Electrical Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia (2016)

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Silvio Giancola

Silvio Giancola is a Research Scientist at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Bernard Ghanem in the Image and Video Understanding Laboratory (IVUL), part of the Center of Excellence for Generative AI (GenAI).

Sherif Abdelkarim

Current Sherif's reserach interests are in application of visual relationship understanding on long tail datasets. Education and Early Career Sherif obtained his bachelor's degree from the German University in Cairo (GUC), Egypt in 2013 in mechatronics engineering. Afterwards he attended MSc program of Intelligent Systems at Aberystwyth University, UK and where he graduated with distinction. Then Sherif worked as machine learning and deep learning engineer for multiple companies on a wide range of applications including customers' behavior clustering, object detection for self driving cars

Shuming Liu

Ph.D. Student, Computer Science
Shuming Liu is a Ph.D. student in the computer science program at the Image and Video Understanding Lab (IVUL), King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Bernard Ghanem.

Shariq Farooq Bhat

M.S. Student, Computer Science
Shariq Farooq Bhat is an MS/PhD student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). Education and Early Career Shariq Farooq Bhat obtained his bachelor of technology degree in Electronics & Communication Engineering from the National Institute of Technology Srinagar, India in 2018. During bachelor’s degree, he was an intern at Texas Instruments (TI) Center for Embedded Product Design at NSIT in New Delhi. Then, in 2016, he worked at the Indian Institute of Science (IISc) as a computer vision and deep learning research Intern and

Sara Rojas Martinez

Sara's research focuses on NeRF and its applications, including scene editing and efficiency.

Sara Shaheen

Sara Shaheen was a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is currently an Assistant Professor in Computer Science at Prince Sultan University (PSU) in Riyadh, Saudi Arabia. Education and Career Sara received her Bachelor degree in Computer Science from Prince Sultan University (PSU) in Riyadh in 2010. She joined the MS/PhD program in KAUST and earned her Master and Ph.D. in Computer Science in 2017. She was a part of Image and Video Understanding

Salman Alsubaihi

Salman Alsubaihi was an Electrical and Computer Engineering Master Student in the Image and Video Understanding Lab (IVUL) under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Salman Alsubaihi obtained his bachelor degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in Saudi Arabia in 2017. In 2019, he received his Master in Electrical Engineering from KAUST. Research Interests ​ Salman is focusing on understanding deep neural

Rui Li

Rui Li is an Electrical Engineering Ph.D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Rui Li obtained bachelor of engineering degree in Telecommunication Engineering from Xidian University in China in 2013. After that in 2016, he received his master degree in Electrical Engineering from University of Science and Technology of China in 2016. Rui started his career life as a Research Assistant at State

Rameen Abdal

Ph.D. Student, Computer Science
Rameen Abdal is a Master student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). He has graduated in 2023. Education and Early Career Rameen Abdal obtained his bachelor of technology degree in Electronics and Communications Engineering from the National Institute of Technology in Srinagar, India in 2018. After that, he joined KAUST for the MS/PhD program in computer science to continue his studies. Before joining KAUST, Rameen was an intern at Texas Instruments (TI) Center for Embedded Product Design at Netaji Subhas University of

Peter Wonka

Professor, Computer Science
Professor Peter Wonka is a computer scientist with a research focus in computer vision, computer graphics, remote sensing, and machine learning.

Rana Muhammad Bilal

Rana Muhammad Bilal is a Researcher in the Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Rana's research interests include ​Algorithms and systems for process control, Hybrid Location tracking frameworks, Embedded System Design and Signal Processing and Computer Vision. Education Profile ​M.S. in Electrical Engineering, National University of Science and Technology, Islamabad, Pakistan. B.S. in Electronic Engineering, GIK Institute of Science and Technology, Pakistan.

Qizhou Wang

Qizhou Wang research focuses on advancing optical sensing, nanophotonics, and intelligent computational imaging.

Qian Wang

Ph.D. Student, Computer Science
Qian Wang is a MS/Ph.D. student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Wonka in his research group. Education Qian Wang graduated with great distinction from Wuhan University with Bachelor's Degree. Research Interest Research interest of Qian Wang is Computer Vision.

Peihao Zhu

Ph.D. Student, Computer Science
Peihao Zhu is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. He has completed his Ph.D and graduated in 2023. Education and Early Career Peihao Zhu obtained his bachelor of engineering in Automation from Northeastern University in China in 2016. After that, he continued his studies in Computer Science and received his master's degree from King Abdullah University of Science and Technology (KAUST) in Saudi Arabia in 2019. Research Interest

Peng Han

Ph.D. Student, Computer Science
Peng Han is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Prior to that, he was a Research Associate in the School of Computer Science and Engineering, Nanyang Technological University (NTU) under the supervision of Prof. Aixin Sun, Singapore. He received his M.E. in Computer Science from Renmin University of China (RUC) at BDAI lab (leaded by Prof. Ji-rong Wen) in 2017. Research Interests ​Peng's research interests

Noor Bafageeh

Noor Bafageeh was a Master student in Computer Science Program under the supervision of Professor Bernard Ghanem at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Education and Career Noor earned her bachelor degree in Computer Science from Effat University in Jeddah in 2016. She joined KAUST and received her master degree in Computer Science in 2018. Research Interest She is interested in computer vision, machine learning, Generative Adversarial Networks (GAN) and deep learning. Education Profile M.S., Computer Science, King Abdullah University of

Neil Smith

Research Scientist, Computer Science
Neil Smith is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Neil joined University of California in San Diego (UCSD) at the start his academic journey. He obtained his bachelor degree in Anthropology with Concentration in Archaeology in 2001. After that in 2003, he received his master degree in Anthropology. He has a doctoral in Anthropology since 2009. Dr. Smith started his career life as a Geographic Information Systems (GIS) Specialist at

Motasem Alfarra

Motasem Alfarra is an Electrical and Computer Engineering Ph.D. student in Image and Video Understanding Lab (IVUL) Group in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem.

Mojtaba Alshams

KAUST-CEMSE-CS-Vision-CAIR-Mohamed-Elhoseiny

Mohamed Elhoseiny

Associate Professor, Computer Science
Professor Elhoseiny’s research focuses on developing affective artificial intelligence that understands and generates novel visual content. He has contributed to and led numerous seminal works of affective AI art creation.

Modar Alfadly

Ph.D. Student, Computer Science
Modar Alfadly is a Ph. D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Alfadly received his bachelor’s degree in Software Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2016. A year later, he obtained his master’s degree in Computer Science and successfully defended his thesis, titled “Analytic Treatment of Deep Neural Networks under Additive Gaussian Noise”. Research Interest Modar Alfadly is focusing on understanding deep neural networks to build more intelligent

Merey Ramazanova

Postdoctoral Research Fellow, Computer Science

Mengmeng Xu (Frost)

Mengmeng Xu (Frost) was an Electrical and Computer Engineering PhD Student in the Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. He is currently continuing his Ph.D. Education and Early Career Mengmeng obtained his bachelor's degree in Optics and Optical Sciences from Zhejiang University in China in 2017 and his Master's Degree from KAUST under the supervision of Professor Bernard Ghanem. Research Interests He is mainly focusing on problems that arise in computer vision, including

Matthias Mueller

Matthias Mueller was an Electrical and Computer Engineering Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group at Visual Computing Center (VCC) under the supervision of Prof. Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Matthias graduated with a B.Sc. in Electrical Engineering and Math Minor from Texas A&M University in 2011. After graduation, he joined P+Z Engineering in Munich, Germany as an Electrical Engineer and worked for 3 years on the development of mild-hybrid electric machines at BMW. In 2014, he started his M

Matheus Vinícius Medeiros de Souza

Ph.D. Student, Computer Science
Matheus Vinícius Medeiros de Souza is an MS/Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich. Before joining KAUST, Matheus obtained a bachelor's degree in electrical engineering from the Federal University of Rio Grande do Norte, Brazil. Research Interests Matheus's research interests include Computer Vision and Machine learning.

Masheal Alghamdi

Masheal Alghamdi is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Masheal obtained her bachelor degree in Computer Science from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia in 2009. After that she joined KAUST to receive her master degree in Computer Science in 2014. Masheal is a researcher in the National Center for Computation Technology and Applied Math (CTAM) at King Abdul-Aziz City for Science and Technology (KACST)

Lia Coleman

Lia Coleman is a researcher and artist at VISION-CAIR, the Creative AI Research lab at the Visual Computing Center (VCC) of King Abdullah University of Science and Technology (KAUST). Education and Early Career Lia Coleman received her B.Sc. in Computer Science at Massachusetts Institute of Technology (MIT) in 2017. Since then, she has worked as a computer vision engineer, and is now researching novel art generation using GANs with Prof. Mohamed Elhoseiny of KAUST. In addition to her technical accomplishments, Lia is also a talented multidisciplinary artist. She has exhibited her work at MIT's

Lama Alssum

Ph.D. Student, Computer Science
Lama Alssum is a Ph.D. student in Computer Science at King Abdullah University of Science and Technology (KAUST) working in Professor Bernard Ghanem research group Image and Video Understanding Lab (IVUL).

Kumail Alhamoud

M.S. Student, Computer Science
Kumail Alhamoud is a Computer Science M.S Student at KAUST working under the supervision of Professor Bernard Ghanem at the Image and Video Understanding Lab (IVUL). Before joining KAUST, Kumail obtained his B.S in Electrical and Computer Engineering at Cornell University in 2021. His current research interests include continual learning, learning from limited data under distribution shifts, and AI for Science and Biomedicine. Publications

Kilichbek Haydarov

Ph.D. Student, Computer Science
He is a Computer Science PhD researcher at KAUST, specialising in affective vision–language modelling, generative AI, and Neuroscience + AI. His work bridges multimodal emotion understanding, generative models, and neural signal modelling to advance human-centred and scientifically grounded AI systems.

Khaled Abu-Jbara

Khaled Abu-Jbara was a Ph. D. student in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Khaled Abu Jbara received his bachelor degree in Electrical Engineering, with Distinction, First Class Honors, from Kuwait University (KUNIV), Kuwait in August 2011. He worked as an Administrative Engineer and Teacher Assistant at Kuwait University, Electrical Engineering Department. After that, he joined King Abdullah University of Science and Technology (KAUST) as a

Kai Yi

Ph.D. candidate working on machine learning optimization, LLMs and federated learning.

Juan C. Pérez

Juan C. Pérez was a Ph.D. Candidate in the computer science program under Professor Bernard Ghanem's supervision at the Image and Video Understanding Lab at King Abdullah University of Science and Technology (KAUST). Before joining KAUST, Juan obtained a master's degree and a bachelor's degree ( cum laude) in biomedical engineering from Universidad de Los Andes, Colombia.

Juan Pablo Peña (Student)

Juan Pablo Peña is part of the Visiting Students Research Program at King Abdullah University of Science and Technology (KAUST). During his Internship, he is conducting his research at the Sensors Lab, in the Electrical Engineering Department, CEMSE, KAUST. Education and Early Career Juan Pablo Peña graduated from Mecatronichs Engineering B.S. at the National Autonomous University of Mexico, Mexico City on May 2019. Additionally, on 2019 summer he worked on an augmented reality simulation for visual impairments created during his research internship at Ryerson University, Canada. Similarly, on

Jia-Hong Huang

Jia-Hong Huang was a Master's student in the Electrical and Computer Engineering Program under the supervision of Professor Bernard Ghanem at Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Career Jia-Hong earned a double major bachelor's degree in Statistics and Mathematical Science from National Chengchi University in Taiwan in 2014. He received his master's degree in Mechanical Engineering from National Taiwan University in 2016. After that, he joined KAUST and received his second master's degree in Electrical Engineering in 2018

Jean Lahoud

Jean Lahoud was a Ph. D. student in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Jean Lahoud received his Bachelor of Engineering in Mechanical Engineering from Notre Dame University in Lebanon in 2012. In 2014, he obtained his Master of Engineering degree in the same field from American University of Beirut in Lebanon. Before joining KAUST, Jean was a product manager at Murex systems in Lebanon. Research Interest Jean Lahoud is interested in 3​D scene understanding using RGB-D sensors. Honors and Awards In

Jesus Zarzar

Jesus Zarzar was an Electrical and Computer Engineering PhD student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Jesus obtained his bachelor degree in Electrical and Computer Engineering from UT Austin, United States in 2017 before obtaining his Master's Degree from KAUST under the supervision of Professor Bernard Ghanem. Publications Education Profile ​B.S., Electrical and Computer Engineering, UT Austin, Austin, United States, 2017

Ivan Skorokhodov

Ph.D. Student, Computer Science
Ivan Skorokhodov is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Peter Wonka at Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). His research interests include such areas of deep learning as generative models, hypernetworks and neural rendering. Ivan Skorokhodov has obtained his Ph.D degree and graduated in March 2023. Education and Early Career Ivan obtained his bachelor's degree in Management from the Moscow Engineering Physics Institute in 2014. He continued his studies there and received his master's degree

Humam Alwassel

Humam Alwassel was a Computer Science Ph.D. student in the Image and Video Understanding Lab (IVUL) Group under the supervision of Professor Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Humam obtained his bachelor's degree with a double major in Computer Science and Mathematics from Cornell University in New York, USA, in 2016. After that, he joined KAUST for the MS/PhD program and received his master's degree in Computer Science in 2018, and his Ph.D degree in 2023. Research Interest He’s currently focused on the development of novel

Hasan Abed Al Kader Hammoud

Hajer Abbas

Visiting Student (former), Electrical and Computer Engineering
Visiting Student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST).

Habib Slim

Ph.D. Student, Computer Science
Habib Slim is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Education: M.Sc Data Science, Grenoble Institute of Technology, France, 2021 M.Eng Applied Mathematics and Computer Science, Grenoble Institute of Technology, France, 2021

Gordon Guocheng Qian

Guocheng Qian is currently working towards an MS/Ph.D. degree in the Department of Computer Science at King Abdullah University of Science and Technology (KAUST). He is part of the Image and Video Understanding Lab (IVUL), supervised by Prof. Bernard Ghanem. During his Ph.D. studies, he interned at several leading institutions including Snap Research, Meta Reality Lab, and MEGVII research. His primary research interest lies in 3D vision. Education and Early Career Guocheng obtained his bachelor's degree in Engineering with first-class honors from Xi’an Jiaotong University (XJTU) in China in

Guohao Li

Guohao Li was a Ph. D. student in the Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Guohao Li obtained a Bachelor’s Degree in Communication Engineering from Harbin Institute of Technology in China in 2015. In 2018, he earned his Master’s degree in Communication and Information Systems from the Chinese Academy of Sciences, China, and in 2023, he received his PhD in Computer Science from KAUST. Research Interest His primary research interest is in Computer Vision and Deep Learning. Publications Education Profile

Ganesh Sundaramoorthi

Ganesh Sundaramoorthi was an Assistant Professor of Electrical Engineering and jointly Assistant Professor of Applied Mathematics and Computational Science at KAUST starting in 2011. He is currently Principal Research Scientist at United Technologies Research Center in East Hartford, CT, USA, formulating computer vision algorithms for robotic inspection applications. Education and Early Career His PhD is in Electrical and Computer Engineering from the Georgia Institute of Technology in Atlanta, GA, USA in 2008. His PhD developed fundamental shape optimization methods for computer vision that

Frankly Toro

Frankly Toro is a Ms/Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career He is a graduate of National University of Colombia where he received a B.S. degree in Mechatronics. Education Profile B.S., Mechatronics , National University of Colombia, 2020

Fabian Heilbron

Fabian Caba Heilbron was a Ph.D. student in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). He is currently a Research Scientist at Adobe Research in San Jose, California. Education and Early Career Fabian has a Bachelor’s Degree in Electronics Engineering from Universidad del Norte in Barranquilla, Colombia, 2011. In 2013, he received his Master Degree in Electronics Engineering from Universidad del Norte in Barranquilla, Colombia. He joined KAUST as a Ph.D. candidate and received his

Chen Zhao

Chen Zhao was a Research Scientist at KAUST, and Lead of the Video Understanding Theme in the Image and Video Understanding Lab. She received her Ph.D. degree from Peking University, China in 2016. She studied in University of Washington, US from 2012 to 2013, and at the National Institute of Informatics, Japan in 2016. She has published 40+ papers on representative journals and conferences such as TPAMI, CVPR, ICCV, ECCV, and has received over 3000 citations according to Google Scholar.

Cheng Zheng

Cheng Zheng is a Software Engineer Consultant working with Professor Bernard Ghanem in the Image and Video Understanding Lab (IVUL) in the Visual Computing Center at King Abdullah University of Science and Technology (KAUST). Education and Early Career Cheng Zheng received his Bachelor's degree in Computer Science from Beijing University of Posts and Telecommunications (BUPT), China, in 2009. He received his Master's degree from the University of Chinese Academy of Sciences (UCAS), China, In 2012. Before joining KAUST, in 2012, he worked as a software engineer at NetEase Games in China. In

Caigui Jiang

Research Scientist, Computer Science
Caigui Jiang is a Research Scientist at the Visual Computing Center (VCC) working with Professor Helmut Pottmann at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Jiang obtained a bachelor degree in Electronic Engineering and his masters in Pattern Recognition and Intelligent Systems from Xi'an Jiaotong University (XJTU) in China. After that, he joined KAUST to receive his Ph.D. in Computer Science in 2016. Caigui has a diverse experience in the academic and research field. In 2016, he joined Max Planck Institute for Informatics as a Postdoctoral

Carlos Hinojosa

Education and Early Career o Bachelor of System Engineering and Informatics, University: Universidad Industrial de Santander, 2011-2015. o Master of Science in Computer and System Engineering, University: Universidad Industrial de Santander, 2016-2018. o PhD in Computer Science, University: Universidad Industrial de Santander, 2017-2022.

Boris Radovic

Boris Radovic is a Ph.D. candidate in the computer science program, under the supervision of Professors Marco Canini and Veljko Pejović, and is a member of the Software-defined Advanced Networked and Distributed Systems (SANDS) Lab. He holds a Bachelor of Science (B.Sc.) in Computer Science and a Master of Science (M.Sc.) in Data Science, both obtained from the Faculty of Computer and Information Science at the University of Ljubljana, Slovenia. During his academic journey, Boris gained experience across diverse computer science domains through his long-term collaboration with the Laboratory

Ali Thabet

Ali Thabet is a Research Scientist at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), working with Professor Bernard Ghanem in the Image and Video Understanding Laboratory (IVUL). Education and Early Career Ali obtained a bachelor of engineering degree in Electrical Engineering, from the American University of Beirut in Beirut, Lebanon in 2006. He holds a ​Ph.D. in Computer Science from the University of Dundee in Dundee, UK. He joined KAUST as a Postdoctoral Fellow in the Visual Computing Center (VCC) from 2012 to 2014. After that, he worked as

Alejandro Pardo

Alejandro Pardo is a Ph.D. candidate in the Electrical and Computer Engineering (ECE) program under the supervision of Professor Bernard Ghanem at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia.

Ahmed S. Abdelreheem

Ahmed Abdelreheem is a Computer Science MS. student at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Ahmed Abdelreheem is a graduate of Cairo University, where he received a B.S. degree in Computer Engineering in 2019. Research Interest Ahmed focuses on Computer Vision and Machine Learning. Education Profile BSc., Computer Engineering, Cairo University, Egypt, 2019

Abdurauf Abdurakhmanov

Adel Bibi

Adel Bibi is a postdoctoral research assistant in computer vision and machine learning at the Torr Vision Group working with Professor Philip Torr at the University of Oxford. Prior to that, he received his MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) where he was part of the Image and Video Understanding Lab (IVUL) advised by Professor Bernard Ghanem. Education and Early Career Adel Bibi has a Bachelor’s Degree in Electrical Engineering from Kuwait University in 2014. He received his Master Degree in Electrical Engineering from King Abdullah University of

Abdullah Hamdi

Abdullah Hamdi was a Ph.D. student in the Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career In 2016, Abdullah Hamdi obtained his bachelor's degree in Electrical Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran, Saudi Arabia. After that, he joined KAUST to pursue the same field of study and received his master's degree in 2017 in the Computer Vision track. Research Interest Machine Learning Robustness of Deep Learning Computer
<> bioinformatics (107)

How data is transforming transplantation in Saudi Arabia

3 min read ·
Every organ transplant is a race against time. The outcome often hinges on finding a biologically compatible donor at the right time. Success depends on a complex combination of factors: organ quality, timing, patient readiness, logistics, clinical practices and long-term risks. Transplantation medicine relies on care coordination among multidisciplinary providers across institutions and on long-term patient monitoring. Improving data systems plays a crucial role in assessing key performance indicators, ensuring timely transplants and determining which patients benefit most from the organ

Data Analytics to Outcome Impact for Transplantation and Clinical Practice

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B3 R5209

Workshops
Leveraging national data analytics and computational tools to advance transplantation and clinical practice.

Visual Analytics for Macromolecular Science

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This dissertation presents a suite of scientific visual analytics systems that tightly couple large-scale computational modeling - including both AI-powered inference and traditional molecular simulations - with interactive, scalable visualization.

Bridging AI and Spatial Biology to Navigate Biological Landscapes

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This thesis presents several studies by proposing AI-driven methods to supply a comprehensive and precise representation of biological landscapes via spatially resolved transcriptomics (SRT) data.
AI tool maps hidden links between diseases

AI tool maps hidden links between diseases

1 min read ·
Understanding how diseases trigger one another could lead to better treatments, prevention strategies and improved genetic risk prediction.

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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B3 L5 R5209

Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

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B3 L5 R5209

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

Computational Methods for ChIP-seq Data Analysis and Applications

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B3 L5 5209

Abstract The development of Chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology has enabled the construction of genome-wide maps of protein-DNA interaction. Such maps provide information about transcriptional regulation at the epigenetic level (histone modifications and histone variants) and at the level of transcription factor (TF) activity. This dissertation presents novel computational methods for ChIP-seq data analysis and applications. The work of this dissertation addresses four main challenges. First, I address the problem of detecting histone modifications from

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

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B3 R5208

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural Inductive Matrix Completion (NIMC) in disease-gene prediction.

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

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B2 R5220

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

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B2 L5 R5220

Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

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B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Zero in on computational challenges in Nanopore sequencing

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B9 L2 H1

In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. Then, I will further introduce their applications in clinical and environmental fields.

Knowledge-based methods in life sciences

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B9 L2 H2 R2325

In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine. To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

Bio-Hackathon MENA 2023

Bio-Hackathon MENA 2023

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KAUST Hotel

Bioinformatics experts, Don’t miss the opportunity to collaborate with researchers and field professionals in the BioHackathonMENA2023 event. BioHackathon events involve a large number of people that meet on-site to discuss ideas and implement projects in a collaborative manner during intensive coding sessions.

Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations

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B3 L5 R5220

We propose a structured latent ODE model that explicitly captures system input variations within its latent representation. Building on a static latent variable specification, our model learns (independent) stochastic factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space. This approach provides actionable modeling through the controlled generation of time-series data for novel input combinations (or perturbations). Additionally, we propose a flexible approach for quantifying uncertainties, leveraging a quantile regression formulation.

InfoCloud published in the VLDB Journal a paper "ACME: A scalable parallel system for extracting frequent patterns from a very long sequence"

1 min read ·
InfoCloud published in the VLDB Journal a paper "ACME: A scalable parallel system for extracting frequent patterns from a very long sequence."

InfoCloud has published a paper "Karect: accurate correction of substitution, insertion and deletion errors for next-generation sequencing data" in Bioinformatics

1 min read ·
InfoCloud has published a paper "Karect: accurate correction of substitution, insertion and deletion errors for next-generation sequencing data" in Bioinformatics

InfoCloud has published a paper "Progress and challenges in bioinformatics approaches for enhancer identification" in Briefings in Bioinformatics 2015

1 min read ·
InfoCloud has published a paper "Progress and challenges in bioinformatics approaches for enhancer identification" in Briefings in Bioinformatics 2015.

Dimitrios Kleftogiannis has defended his Ph.D. thesis

1 min read ·
Dimitrios Kleftogiannis has defended his Ph.D. thesis "Identification of enhancers in human: Advances in computational studies."

Professor Robert Hoehndorf's PhenomeNET method wins at Ontology matching workshop

1 min read ·
Professor Robert Hoehndorf and Postdoctoral Fellow Miguel Angel Rodriguez Garcia are the winners of the Ontology Alignment Evaluation Initiative Challenge sponsored by Pistoia Alliance.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

3 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Siqing Hou defends his Master’s thesis

1 min read ·
Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

Alumni Feature: Abdullah Khamis

1 min read ·
Abdullah Khamis has had quite an impressive career so far. After graduating with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao, he now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia.

Sarah Alghamdi defends her master's thesis

1 min read ·
Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Maxat Kulmanov defends his thesis proposal

1 min read ·
Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

Computing solutions for biological problems

2 min read ·
Xin Gao’s research is building bridges between computer science and biology.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Azza Althagafi defends her Master's thesis

1 min read ·
Azza Althagafi, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 1, 2018.

Sara Althubaiti defends her Master's thesis

1 min read ·
Sara Althubaiti, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 5, 2018.

Please see our publication page

1 min read ·
The SFB group has been very productive in both bioinformatics/computational biology and artificial intelligence.

Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models

1 min read ·
We present a new technique for rapid modeling and construction of scientifically accurate mesoscale biological models. Resulting 3D models are based on few 2D microscopy scans and the latest knowledge about the biological entity represented as a set of geometric relationships. Our new technique is based on statistical and rule-based modeling approaches that are rapid to author, fast to construct, and easy to revise. From a few 2D microscopy scans, we learn statistical properties of various structural aspects, such as the outer membrane shape, spatial properties and distribution characteristics

Robert Hoehndorf promoted to associate professor at KAUST

4 min read ·
KAUST faculty member Robert Hoehndorf was recently promoted from the rank of assistant professor to associate professor. Hoehndorf’s promotion caps a year-long process where the German researcher’s scientific and scholarly output was measured and evaluated by internal and external reviews.

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

3 min read ·
KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

Xin Gao promoted to rank of professor

1 min read ·
Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

CBRC congratulates Aryam Alotaibi a SRSI student on winning the top paper Award

1 min read ·
Aryam Alotaibi a SRSI student at CBRC wins the top paper Award titled "Computational method to predict drug-disease associations using machine learning and graph embedding."

MS Thesis Defense: Rare variant collapsing analysis on UK Biobank by Yang Liu

1 min read ·
Yang Liu, M.S./Ph.D. candidate in the Bio-Ontology research group under the supervision of Professor Robert Hoehndorf successfully defended her thesis "Rare variant collapsing analysis on UK Biobank".

KAUST alumnus named in Forbes' “30 Under 30 Asia list” – Class of 2022

3 min read ·
KAUST alumnus Yu Li (Ph.D. '20) was recently named amongst the Forbes' "30 Under 30 Asia list" – Class of 2022. Forbes recognized the current assistant professor of computer science and engineering at The Chinese University of Hong Kong (CUHK) for his work developing algorithms to solve problems in biology and health care.

MS Thesis Defense | Learning 3D structures for protein function prediction

1 min read ·
Student Nurul Muttakin from the Bio-Ontology Research Group (BORG) under the supervision of Assoc. Prof. Robert Hoehndorf successfully defends his Masters thesis. He presented his research on the topic " Learning 3D structures for protein function prediction".

A new approach to rare disease diagnosis

2 min read ·
An innovative AI-based tool leverages varied symptom descriptions, along with evidence from the scientific literature and genomic datasets, to pinpoint disease-associated gene variants.

AI tool predicts function of unknown proteins

2 min read ·
Powered by logic and large language models, DeepGO-SE predicts the biological role of proteins and could be used to aid drug discovery.

Zhirun Li

Zhirun Li is a M.S./Ph.D. student at King Abdullah University of Science and Technology (KAUST). He obtained bachelor degree of Optoelectronic Information Science and Engineering from University of Electronic Science and Technology of China (UESTC) in 2019. Education Profile M.S./Ph.D. in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, Aug. 2020 - present B Eng. in Optoelectronic Information Science and Engineering at University of Electronic Science and Technology of China (UESTC), Chengdu, China, Sept. 2015 - Jun. 2019
KAUST-CEMSE-CS-SFB-Zhongxiao-Li

Zhongxiao Li

Ph.D. Student, Computer Science
Zhongxiao Li is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST).​ Zhongxiao obtained his Bachelor of Science degree from Southern University of Science and Technology, China in 2018. Research Interests Zhongxiao's research interests include Bioinformatics and Computational Biology.

Zhenzhen Zou

Zhenzhen Zou worked as a ​Researcher at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Zhenzhen's research interests included Bioinformatics and Machine learning.

Zainab A. Homoud

M.S. Student, Bioengineering
Research Interest Studying : Brain time series, fMRI, EEG, Signal Processing and Machine Learning for better Mental Disease Diagnosis and Consciousness Study. Previous Education Undergraduate study (B.S) in Biomedical Engineering and Health Science at the Joint program of North Carolina State University and UNC Chapel Hill.

Yulian Wu

Ph.D. Student, Computer Science
Yulian Wu is a Ph.D. candidate under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST).

Yongkang Long

Ph.D. Student, Computer Science
Yongkang Long is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Yongkang's research interests include Bioinformatics and Computational Biology.

Yu Li

Ph.D. Student, Computer Science
Research Interests Yu Li is a Ph.D. student at KAUST, majoring in Computer Science. His advisor is Prof. Xin Gao. His research interests are Deep Learning, Machine Learning, and Bioinformatics. He got his Master degree in Computer Science at KAUST in December 2016. Before that, he got his Bachelor degree in Biosciences at the University of Science and Technology of China (USTC). Scientific and Professional Membership Association for the Advancement of Artificial Intelligence(AAAI) Member International Society for Computational Biology (ISCB) Member Education Profile Ph.D student, Computer

Yang Liu

Ph.D. Student (former), Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

Xuefeng Cui

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Xuefeng Cui worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2014-2016. Currently, Xuefenga is a Professor at Shandong University, China. Education Profile Ph.D. degree, University of Waterloo, Canada, Jan 2007 - April 2014 (except May-Aug, 2012 and May-Aug, 2013). Master's degree, University of Waterloo, Canada, Sep 2004 - Dec 2006. Bachelor's degree, University of Waterloo, Canada, Sep 2000 - Apr 2004. Columbia International College, Canada, Sep 1999 - Aug 2000

Xin Gao

Professor, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaopeng Xu

Postdoctoral Research Fellow, Computer Science
Xiaopeng Xu is a Postdoctoral Fellow in the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao. Before joining Postdoctoral Fellow program, Xiaopeng obtained Ph.D. and master’s degree in computer science from King Abdullah University of Science and Technology (KAUST), and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. Research Interests Xiaopeng's research interests included Bioinformatics, artificial intelligence and computational biology.

Wenkai Han

Ph.D. Student, Computer Science
Wenkai Han is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Wenkai obtained his Bachelor of Science degree from the University of Science and Technology of China in 2018. Research Interests Wenkai's research interests include Bioinformatics and Computational Biology.

Trisevgeni Rapakoulia

Trisevgeni Rapakoulia obtained her Ph.D. degree (2016-2019) in Computer Science under the supervision of Professor Xin Gao at structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Trisevgeni's research interests include bioinformatics, System Biology, Prediction of harmful SNPs, Genome-Wide Association Studies and Artificial Intelligence, Machine Learning Approaches, Evolutionary Algorithms, Feature Selection Techniques. Research field: study the effects of drugs and drug combinations in the transcriptome level

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Tilman Hinnerichs

Visiting Student (former), Bio-Ontology Research Group
Tilman Hinnerichs was a visiting student at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Research Interests Tilman's research interests included ​Machine Learning, Bioinformatics, Ontologies, Reasoning, Logic, Particle Methods. He was particularly interested in the application of machine-learning algorithms to problems of the bioscience field. Education Profile ​Since 2016: Diploma Student, Computer Science, TU Dresden​. 2015-2016: Student, Human Medicine, Charité Berlin.

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Shrihari Sonavane

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Shrihari Sonavane worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2013-2014. Shrihari received his Ph.D. degree from Jadavpur University, India. Research Interests Shrihari's research interests included Bioinformatics and computational biology. Selected Publications 1. Dey Sucharita, Pal Arumay, Guharoy Mainak, Sonavane Shrihari and Chakrabarti Pinak (2012) Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Shahad Qathan

M.S. Student (former), Computer Science
Shahad Qathan is an MS candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Artificial Intelligence, more specifically Machine Learning and its applications in healthcare and Bioinformatics Education Profile B.Sc, Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, 2020

Senay Kafkas

Research Scientist (former), Bio-Ontology Research Group
Research Interests Senay's research interests include biomedical text/data mining, Natural Language Processing (NLP), and Next Generation Sequencing (NGS) data analysis. Scientific and Professional Membership ​International Society of Computational Biology (ISCB) (2010-present). KAUST Affiliations Bio-Ontology Research Group (BORG).​ Computational Bioscience Research Center (CBRC). Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE). Education Profile Ph.D., Computer Engineering, Eastern Mediterranean University, Cyprus, 2012. M.Sc., Computer Engineering, Eastern

Sara Althubaiti

Ph.D. Student (former), Computer Science
Sara Althubaiti is currently a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in

Rund Tawfiq

Ph.D. Student (former), Bioengineering
Research Interests My research interests include bioinformatics and using computational methods for data analysis in biology. I am interested in working on biological questions that require multi-omics data integration. I am also interested in neuropharmacology and the molecular biology underlying neuropsychiatric disorders. Professional Profile 2015-2019: Bachelor of Science in Biology, Psychology and Neuroscience; University of Massachusetts - Amherst 2019-2021: Master of Science in Bioscience; King Abdullah University of Science and Technology 2021-present: Ph.D. student; King Abdullah

Robert Hoehndorf

Associate Professor, Computer Science
Robert Hoehndorf is an Associate Professor of Computer Science at KAUST. His research focuses on bioinformatics, knowledge representation and reasoning, Semantic Web technologies, and neuro-symbolic methods.

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Rahaf Shiqdar

Visiting Student (former), Bio-Ontology Research Group
Rahaf Shiqdar was a visiting student from ​Umm Al-Qura University ​at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Rahaf's research interests included ​Bioinformatics, Machine and Deep learning.

Qiming Fang

Prof. Fang is a visiting professor from Hangzhou Dianzi University, China at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Prof. Fang's research interests include Bioinformatics and Machine learning.

Peng Chen

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Chen worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2012-2014. Dr. Peng Chen is currently a Professor at the Institute of Health Sciences, Anhui University. Prior to Anhui University, he was an Associate Professor at the Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei Anhui, P.R.China. He received his Ph.D. degree in Pattern Recognition and Intelligent System from the University of Science and Technology of China, and Bachelor and

Peng Yang

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Yang worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Peng is a research scientist at Baidu Research. Research Interests ​Machine Learning and Data Mining problem, especially online active learning, multi-task learning, recommender systems, and deep learning. Algorithms for bioinformatics problems, especially in disease gene prediction, drug-target interaction prediction, protein complex, and microbial community pattern detection

Othman Soufan

Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent

Olga Mashkova

Ph.D. Student, Computer Science
PhD student in Computer Science. Research interests: machine learning, bioinformatics, mathematics, knowledge representation and reasoning, deep learning, algorithms.

Omar Maddouri

Visiting Student (former), Bio-Ontology Research Group
Research Interests: Biological networks and interactions

Maxat Kulmanov

Research Scientist (former), Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Mario Messih

Mario Messih is a Former Visiting student from the Sapienza University of Rome, Italy at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Mario Finished his Ph.D. study with Prof. Anna Tramontano at the Sapienza University of Rome, Italy, and currently, Mario is a postdoc fellow at Yale University. Research Interests Mario's research interests included Bioinformatics and computational biology. His main interests lie at the intersection of machine learning, statistics and their applications on analyzing large

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Luke Slater

Ph.D. Student (former), Computer Science
Luke Slater is working as a Research Fellow in the Institute of Cancer and Genomics with an interest in ontology and ontology accessories, and their combination with other interdisciplinary techniques to gain novel and practically useful insights into information.

Kosuke Goto

Research Interests ​Development of bioinformatics tools and databases for molecular evolution, population genetics, metagenomics. Professional Profile ​2015-present: Technical Specialist, KAUST, Thuwal, Saudi Arabia Education Profile ​M.Sc., Applied Biological Science, Tokyo University of Science, Chiba, Japan, 1993 B.Sc., Physics, Tokyo University of Science, Tokyo, Japan, 1991

Kexin Niu

Ph.D. Student (former), Bioengineering
Kexin Niu, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Kexin obtained her bachelor's degree in Bioinformatics from Southern University of Science and Technology, Shenzhen, China. "What I like best about KAUST is the communication of diverse cultures and diverse disciplines, which enables me to have more possibilities." Research Interest Kexin’ s Research interests include but are not limited to bioinformatics and application of machine learning method in biomedical data. Education Profile 2016 -

Jun Chen

Ph.D. Student, Computer Science
Jun Chen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Mohamed Elhoseiny at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jun obtained his bachelor's degree in Computer Science from Xi’an Jiaotong-Liverpool University in China, 2018. He joined KAUST as a Master student under the supervision of Prof. Robert Hoehndorf and received his master's degree in Computer Science in 2019. Research Interests Jun's research interests include machine learning, artificial intelligence, natural language processing, knowledge

Jordy Homing Lam

Jordy Homing Lam worked as a ​Research Associate at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST) during 2018-2019. Currently, Jordy is a Ph.D. student at USC. ​Jordy Homing Lam obtained his Master's Degree from Hong Kong University of Science and Technology. Research Interests Jordy's research interests included Bioinformatics, Computational Biology and Molecular Dynamics Simulation.

Heba Abu Samra

Heba Abu Samra obtained her Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis (2013): A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data Research Interests Heba's research interests included Bioinformatics and Computational Biology.

Haoyang Li

Ph.D. Student, Computer Science
Haoyang's research focuses on developing AI-driven methods to integrate multimodal biomedical data, spanning spatial multi-omics, histopathology, and single-cell genomics, with the goal of decoding spatial heterogeneity in diseases and advancing diagnosis, prognosis, and biomarker discovery for personalized therapies.

Hajime Ohyanagi

Research Interests ​My original question in biology is the Origin and History of Eukaryotes. I also love to understand the possibility of Genomic Breeding in the imminent global food security issue. To these ends, currently, I aim to determine the biological significance in big data of marine and crop genomics, by the means of diverse bioinformatics methodologies. Professional Profile 2015-present: Technical Specialist, KAUST, Thuwal, Saudi Arabia 2015-present: Visiting Research Scientist, National Agriculture and Food Research Organization, Tsukuba, Japan 2013-present: Visiting Research

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc

Fernando Patricio Zhapa-Camacho

Postdoctoral Research Fellow, Computer Science
Fernando Patricio Zhapa Camacho is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST, Fernando obtained a bachelor's degree in Information Technology from Yachay Tech University, Ecuador. Research Interests Fernando’s research interests include knowledge representation and reasoning, machine learning and bioinformatics. He is interested in artificial intelligence and logic integration with applications in biology. Professional Profile: • 2020 to present: Graduate Student, KAUST, Thuwal, Saudi Arabia. •

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Dimitrios Kleftogiannis

Dimitrios Kleftogiannis obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group in King Abdullah University of Science and Technology (KAUST), in 2016. Research Interests Dimitrios works mostly on knowledge discovery technologies with applications in Bioinformatics and Computational Biology. ​His research interests include Machine Learning, Artificial Intelligence and Data Analysis of NGS-derived data. He has worked extensively on the prediction of distal DNA regulatory elements focusing on the category of enhancers. He is

Deng Luo

Ph.D. Student, Computer Science
Deng Luo is a Ph.D. Candidate in the Computer Science program under the supervision of Professor Ivan Viola at the Visual Computer Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia.

Christophe Van Neste

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Christophe Van Neste is a Postdoctoral Fellow under the supervision of Professor Xin Gao at the Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests ​My topic interests are centered around cancer and development, specifically related to the embryonal cancer neuroblastoma and how it links to the normal development of the sympathetic neuronal system. My focus there lies on replicative stress and secondary DNA structures such as G-quadruplexes and how that impacts development or cancer stem cell maintenance. My

Arwa Bin Raies

Research Interests I am a computer scientist whose primary focus is on developing novel machine learning methods to extract useful information from complex datasets. My experience, however, has covered intellectually wide-ranging areas spanning several disciplines in computer science, biology, and chemistry. At the undergraduate level, I focused on computer networks, and I did an internship for six months at Cisco Systems Inc., and I achieved CCNA and CCNP certification. At the Master's level, however, I joined the Computational Bioscience Research Center. I was in charge of a text-mining

Ahmed Abbas

Ahmed Abbas obtained his Ph.D. degree under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Currently, Ahmed is an Assistant Professor at Ain Shams University, Egypt. Research Interests Ahmed's research interests included Bioinformatics, NMR protein structure determination. Selected Publications 1. Carlo Cannistraci, Ahmed Abbas, and Xin Gao. (2015). “Median modified Wiener filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra”. Scientific Reports (Nature

Abeer Almutari

M.S. Student (former), Computer Science
Abeer Almutari obtained her Master's degree in Computer Science under the supervision of Professor Robert Hoehndorf at Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Abeer's research interests included bioinformatics, machine learning and building new automated models using artificial intelligence. Education ​B.Sc. Electrical & Computer Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia, 2016

Abdullah Khamis

Abdullah Khamis graduated with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao. He now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia. He leads the Data Science team in developing several machine learning based predictive models such as sentiment analysis and prediction. Research Interest Abdullah's research interests were in bioinformatics and machine learning.

Abdullah Muhammad Kamal

Visiting Student (former), Computer Science
Abdullah Muhammad-Kamal is a visiting student from the University of Warwick, United Kingdom at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Currently on his placement year, interning with the Bio-Ontology Research Group at KAUST, he will be graduating with his Bachelor's degree in Computer and Management Sciences in the summer of 2021. Research Interests ​Abdullah's research interests include Bioinformatics, Machine Learning, and Data Science.
<> electrical engineering (98)

Blue LED material extended to Green, Yellow, and now Red

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KAUST

In this seminar, the science of MOCVD, the material science of InGaN, and the new-born InGaN-based red LED performance will be discussed. The three primary colors in light are RGB. Green and blue LEDs have been realized by using InGaN active region. The current red LEDs are based on AlGaAs or InGaP as the active region. If we can realize red LEDs by InGaN, it is possible to integrate RGB LEDs in a wafer. Such RGB integration is a breakthrough to develop the next displays, so-called, micro-LED displays that are the next after the OLED displays, and functional LED lightings.

Windows could take the heat

1 min read ·
Generators embedded in windows produce electricity from the temperature differential between an air-conditioned interior and a sunny outside world.

A different kind of light

1 min read ·
The discovery of the incandescent light bulb has transformed human existence. New LED technologies promise to be the next step forward.

Targeting cancer cells with tiny magnetic wires

1 min read ·
Magnetic nanowires with weak magnetic fields and low frequencies can destroy cancer cells without generating heat.

Signal noise annoys no more

1 min read ·
Identifying noise in communication signals helps to filter out glitches and improve transmission quality.

Better routing for wireless networks

2 min read ·
An improved wireless communication protocol enables more robust links between remote electronic devices.

Perfect absorption graphene style

1 min read ·
A tiny device broadens the bandwith to enable absorption with wide-reaching potential for electrical engineering.

Sensors for a light touch

1 min read ·
Biocompatible tactile sensors based on magnetic hair-like structures enable new applications for touch sensors.

Miniature flexible sensor to detect heart disease

1 min read ·
A new biosensor made of laser-etched electrodes on a gold-coated polymer may provide an effective and cost-efficient way to assess heart disease risk.

Smart technology shows where we're at

1 min read ·
A combination of state-of-the-art sensors and standardized data analysis will transform research for tracking animals and humans.

Wireless devices tune-in to cloud power

1 min read ·
Next-generation mobile networks can use cloud computing algorithms to manage the increasingly high data demands of users.

Sneaky losses solved for resistive memory

1 min read ·
A data readout scheme achieves an unprecedented reduction in power consumption for a promising high-performance resistive memory architecture.

Tweaking the perfect recipe

1 min read ·
Adjusting the cooking time is all it takes to tune the magnetic properties of these multi-functional iron nanowires.

Gentle sensors for diagnosing brain disorders

1 min read ·
New sensor design paves the way for safer and more effective brain monitoring.

Color-changing coatings ready for the big time

1 min read ·
The dazzling colors of peacock feathers arise from the physical interaction of light with biological nanostructures. Researchers have discovered how to exploit this natural trickery known as structural coloration into a large-scale printing technology that produces lightweight and ultraresistant coatings in any color desirable.

Driven to diffraction

1 min read ·
Optical elements that diffract light could lead to smaller and more powerful cameras.

Blind matchmaking for more efficient wireless networks

1 min read ·
Autonomously pairing network users could expand the capability of the next generation of wireless networks

Stretching toward energy efficiency

1 min read ·
Deformable thermoelectric materials add a new twist to the design of energy-scavenging devices.

The crest of waveforms for next-gen radar

1 min read ·
A new method for shaping the waveform generated by multi-antenna radar systems is inexpensive and practical.

Fahad's environmental sensor node work is highlighted on KAUST Discovery!​​​

1 min read ·
Fahad's paper ' 3D printed disposable wireless sensor with integrated microelectronics for large area environmental monitoring.' is on the KAUST Discovery page.

Keeping the heat out

1 min read ·
Insights into the thermal behavior of metal-nitride nanowires could open new avenues in optical electronics.

A firefly's flash inspires new nanolaser light

1 min read ·
Synchronized emissions from innovative on-chip lasers create possibilities for inexpensive artificial neural networks.

No strings attached for underwater video system

1 min read ·
An underwater wireless optical communications system for streaming high-quality, live video.

Bringing signals into phase

1 min read ·
A novel type of electronic component made from a blend of polymer materials could enable more effective circuitry.

Semiconductors with an aligned interface

1 min read ·
Efficiency gains come from tuning the properties of semiconducting materials by combining layers of different composition.

Taking control at the junction

1 min read ·
Fine tuning the composition of nitride alloys can further the development of optical and electronic interface devices.

Making waves for ultrahigh definition displays

1 min read ·
Wavy transistors that vertically gain width without increasing their on-chip footprint could drive future flexible displays.

Gaurav's Waste Energy Harvesting work highlighted on KAUST Discovery and other technology websites!

1 min read ·
Gaurav's work on waste energy harvesting through MIM based rectennas has been highlighted in KAUST Discovery with the title "Round The Clock Power From Smart Bowties."

Marine exploration sensing with light and sound

1 min read ·
Unveiling new strategies to improve future wireless underwater sensing networks for marine research and communication.

Building crystals on a very hot surface

1 min read ·
An innovative chemical reactor for depositing semiconductors at very high temperatures draws on the strength of Saudi Arabia’s chemical industry.

Vaseem's Magnetic work highlighted on KAUST Discovery

1 min read ·
Vaseem's Magnetic work highlighted on KAUST Discovery and also got several media attention in technology websites!

Smart skin for flexible monitoring

1 min read ·
An electronic tag that stretches and flexes while it records location and environmental data can monitor marine animals in their natural habitat.

Controlling the crystal structure of gallium oxide

1 min read ·
Precise control of the atomic structure of gallium oxide layers improves the development of high-power electronic devices.

Hussain is taking a flexible approach

1 min read ·
“The theme of our research is how we can empower humanity with technology,” said Muhammad Mustafa Hussain, professor of electrical engineering at KAUST. Using the skills from his time in industry, and the support he is granted by KAUST, Hussain creates flexible, wireless electronics to inexpensively turn everyday objects into smart devices. When Hussain had his novel computer chip technology licensed by Intel Corporation before he had even graduated from his Ph.D., he soon found himself a rewarding job in the semiconductor chip industry. But Hussain harbored the desire to one day go into

Faculty Discuss their Career Journeys and Professional Development

1 min read ·
Join our first session with DR. JÜRGEN KOSEL - Associate Professor of Electrical Engineering CEMSE - PI of Sensing, Magnetism and Microsystems Group This session will be followed by a Q&A discussion. Students across all divisions are welcome. Light snacks will be provided for those who are registered. Register via the Student Life Forum by clicking here Date: Wednesday, Sept. 19th Time: 4:30 – 5:30 pm Location: Student Center, Level 4, Fellowship Hall For more information please contact Office of Professional Development - Professional.Development@KAUST.EDU.SA

Alumni Focus: Wail Bamhair (M.S. '10, electrical engineering)

1 min read ·
Wail Khalid Bamhair (M.S. '10, electrical engineering - nanotechnology) hails from Jeddah and is currently the CEO of TAQNIA Energy, the energy arm of Saudi Investment and Development. Bamhair received his master's degree from KAUST in 2010, the year the University's first class of graduating students were awarded their degrees.

Brighter blue lights for faster communication

1 min read ·
High-performance blue-light-emitting diodes could boost white-light, high-speed data transmission.

Lighting the way to the underwater Internet of things

1 min read ·
An optical system for monitoring underwater sensor positions could enable large networks of devices to be deployed for ocean measurements.

3D-printed cube dials into energy harvesting

1 min read ·
Ambient energy emitted by cellular phones and modems can be captured and converted into electricity using unusually shaped technology.

Marine sensor gets to grips with salt

1 min read ·
Laser-induced graphene formation creates tailor-made sensors for monitoring ocean creatures and ecosystems.

KAUST to launch first virtual fabrication lab

2 min read ·
The vFabLab™ platform, short for Virtual Fabrication Lab, will be presented on April 24th during a webinar organized and hosted by the IEEE Electron Devices Society.

MOFs can sense and sort troublesome gases

1 min read ·
Fluorinated metal-organic frameworks make excellent materials for selective sensing and removal of toxic gases.

Conducting Fab lessons in a virtual clean room

1 min read ·
A virtual platform makes learning how to fabricate a semiconductor device easier, safer and more interactive.

Tiny, fast, accurate technology on the radar

1 min read ·
A tiny, portable radar device could allow visually impaired people, or unmanned moving devices, to detect objects in real time.

Marine Skin dives deeper for better monitoring

1 min read ·
A pioneering tagging system that monitors the movement and local environment of sea animals reaches deeper depths and higher sensitivities.

Smart pill bottle keeps drugs safe

1 min read ·
Low-cost, stretchy sensors can be assembled inside the lid of a drug container to help monitor patient safety.

Flat lenses with a twist!

1 min read ·
Overlaying two film layers patterned with a nanoscale array can manipulate the propagation of light to create a powerful ultrathin lens.

Testing the blind spots in artificial intelligence

1 min read ·
Understanding the situations when artificial intelligence can fail is critical for application of future autonomous vehicles and medical diagnostics.

A cool alternative to air conditioning

1 min read ·
An inexpensive passive cooling technology could be used to cool buildings in cities, reducing energy consumption.

Baby steps for transparent electronics

1 min read ·
Stretchy, see-through, silver nanowire sheet combines optical transparency with excellent electrical conductivity.

Flexing for success from Saudi Arabia to Silicon Valley

2 min read ·
Asrar Damdam is setting up her own biotech company in Silicon Valley while pursuing a Ph.D. at KAUST

Magnetic skin ensures the force is with you

1 min read ·
A magnetic skin that is safe and comfortable to wear could open the door to a wide range of wireless, remotely controlled applications.

Drones deliver on internet connectivity

1 min read ·
Hovering airborne vehicles could connect smart sensors to the internet of things.

Flexible thinking on silicon solar cells

1 min read ·
Combining silicon with a highly elastic polymer backing produces solar cells that have record-breaking stretchability and high efficiency.

Patterned optical chips keep secrets perfectly safe

1 min read ·
Chaos could help put cyberhackers out of business with a patterned silicon chip that will be uncrackable even in the future.

Internet of underground things to reveal buried vistas

1 min read ·
Pinpoint mesh of smart underground objects could give real-time 3D readout of fossil fuel reserves.

A pure twist on optical data transfer

1 min read ·
The finding that light can be twisted very precisely may offer fresh options for communications infrastructure.

Sensor cube helps keep fish farming afloat

1 min read ·
A self-powered water quality sensor could help fish farmers to monitor pollution in their ponds remotely.

Sensing a heart attack before it strikes

1 min read ·
Nanomaterial-based electronic device monitors a key heart health biomarker.

Light sensors detect larval pests munching on date palms

1 min read ·
Optical fibers wrapped around date palm trunks could help detect this tree’s most destructive pest early enough to save it.

Early warning sensor sniffs out cities' harmful gas

1 min read ·
An integrated detector device could form the basis of a distributed air-quality sensor network.

A clique away from more efficient networks

1 min read ·
An old branch of mathematics finds a fertile new field of application.

Iron nanorobots go undercover

1 min read ·
Customizable magnetic iron nanowires pinpoint and track the movements of target cells.

Pure red LEDs fulfill a primary goal

1 min read ·
First high-intensity, low-voltage red LEDs made from nitride semiconductors.

Meet KAUST prospective student: Adilya Bakambekova

1 min read ·
She loves watching films not for the plot, but in order to rethink her beliefs. She is 22, she graduates from the School of Engineering in the capital. Adilya Bakambekova may seem ordinary: she loves walking in the park, powerlifting, taking care of her five-year-old wiener dog.

Unleashing the potential of tethered drones

2 min read ·
Wire-connected drones may complement or replace the fixed base stations of cellular communications networks.

Red-light LEDs for next-generation displays

1 min read ·
Novel red LEDs are more temperature stable than those made using the conventional semiconductor of choice.

Membrane makeover makes face mask reusable

1 min read ·
A polyimide membrane with regularly sized and spaced pores turns the N95 respirator into a reusable mask for protection against COVID-19.

To Boldly Go Where No Sensor Has Gone Before: The Movement to Place IoT in Radical New Spaces

1 min read ·
The rapid growth of the world’s population demands more natural resources, food, and space. World renowned physicist Stephan Hawking has argued that soon we will require another world to live on because we are running out of space and natural resources. This ever-increasing demand for resources and space needs novel technologies to explore natural resources, produce more crops, and explore outer space. Internet of X-things (X-IoT) is an enabling technology to overcome all of the above issues. The framework of X-IoT consists of three major categories as shown in Figure 1. The first one is the

Standing the test of time with a perfect partner

1 min read ·
Identifying the ideal co-catalyst can significantly extend the working lifetime of solar fuel–generating photocatalysts.

KAUST kicks off 12th academic year

1 min read ·
On Tuesday, September 1, 2020, the KAUST community gathered together for the annual academic Convocation ceremony in a manner that has become all too common in recent months—via Zoom. The COVID-19 pandemic has rendered in-person events unsafe, but it has not dimmed the spirit of excitement and optimism of the KAUST community. Student and alumni perspectives "I want to share five things with you that I wish I knew when I started at KAUST," student speaker Nawaf Al-Ghamdi, M.S./Ph.D. student in the CCRC said. "Embrace new experiences; collaborate; be a good teacher; constantly refine your path

A fresh sense of possibility

1 min read ·
A multisensory graphene-based skin can sense in extreme environments where other sensors cannot be used.

Sheer protection from electromagnetic radiation

1 min read ·
A printable ink that is both conductive and transparent can also block radio waves.

Sensors get a laser shape up

1 min read ·
Laser writing breathes life into high-performance sensing platforms.

Intelligent surfaces signal better coverage

1 min read ·
A mathematical model shows specialized reflective panels could be deployed on a large scale to enhance communication networks in urban areas.

KAUST Discovery features "Optoelectronic devices that emit warm and cool white light"

1 min read ·
A single semiconducting material can produce white light by emitting light across the visible spectrum.

Broadening horizons for people with quadriplegia

1 min read ·
An assistive technology uses magnetic skin to support freedom of movement for people with quadriplegia.

Breaking records like baking bread

1 min read ·
Researchers have made unparalleled ultrawide-bandgap semiconductors through temperature and timing, just like baking bread.

Making the shift from blue to red for better LEDs

1 min read ·
Pure red-light micrometer-scale emitting devices made from a nitride semiconductor reaches excellent efficiency.

KAUST Ph.D. candidate wins first place at IEEE SSD’21 PhD Students Contest

2 min read ·
KAUST Ph.D. candidate Mohamed Abdelwaheb Bahloul recently won first place at the IEEE International Multi-Conference on Systems, Signals and Devices 2021 (SSD’21) PhD Students Contest. The contest provided a showcase for students’ original work related to electrical engineering and information technology.

KAUST team power ahead with alternative energy research

2 min read ·
With the continued growth in Internet of Things applications, deployments, and devices, highly connected, sustainable smart cities are no longer mere concepts or fantastical future dreams; many are already active and are expanding apace.

Plant probes make sense for sustainable farming

1 min read ·
Ultrathin needles for probing plants could help keep crop health in check.

Divided attention could ease wireless congestion

1 min read ·
Predicting wireless traffic using artificial intelligence could improve the reliability of future wireless communications.

Full color LEDs cut down to size

1 min read ·
Tiny light-emitting devices that can create all the colors in the rainbow are essential for the next generation of phones and screens.

Airborne antennas to address electro-magnetic fields exposure concerns

1 min read ·
Movable airborne antennas receiving cellphone signals could reduce EMF exposure while offering higher data transmission speed and using less power.

Welcoming composite inks into the fold

1 min read ·
A screen-printing approach to creating foldable circuits could make many functional devices easier and cheaper to mass produce.

Perovskites used to make efficient artificial retina

1 min read ·
KAUST researchers have built an artificial electronic retina that mimics the behaviour of the eye’s rod retina cells. Their artificial retina can recognise handwritten numbers.

Smart memory devices that can sense

1 min read ·
In-memory sensing technology will facilitate electronic systems that are faster, smaller and consume less energy.

Fiber glow brings more go for underwater communications

1 min read ·
KAUST scientists have developed an optical-based wireless network system that has significant potential for faster underwater optical communications.

Ditching sensors for streamlined control of microdevices

1 min read ·
Hossein Fariborzi's team has developed a novel miniature microelectromechanical device that avoids the need to use sensors and has significant potential for use in industrial or medical applications.

Raef Murtadha Almuallem

Raef Almuallem is an undergraduate student at the University of Illinois at Urbana-Champaign, USA. He is studying electrical engineering. Raef joined the Secure Next Generation Resilient Systems Lab (SENTRY) led by Professor Charalambos (Harrys) Konstantinou as a KAUST Gifted Student Program (KGSP) student.

Nurzhan Yesmagambet

I build ROVs that plan their own inspection dives, so reef coverage is decided before the vehicle enters the water instead of left to the pilot. I designed both the planner and the vehicle, and they work on real Red Sea reefs, not in simulation. Robotics systems engineer at KAUST, looking for Lead, Senior or Architect roles in marine robotics.

Moaaz Hasan Fayome

Moaaz Hasan Fayumy is a senior undergraduate student at MIT, specializing in Electrical Engineering and Computer Science. In the capacity of a researcher with the Secure Next Generation Resilient Systems Lab (SENTRY) at KAUST, he has engaged in projects concerning the development of methods for solid-state battery 'fingerprinting', a technique designed to improve hardware security. His work is characterized by an interdisciplinary approach, merging principles of electrical engineering and computer science in the context of real-world challenges. Amid the complexity and technical difficulties

Ibrahim Almuqbil

Internship in King Abdullah University of Science and Technology (KAUST)

Gabriel Eduardo Mejia Ruiz

Gabriel E. Mejia Ruiz received a Ph.D. (Cum Laude) degree in Electrical Engineering from the National Autonomous University of Mexico (UNAM) in 2023. He received the M.Eng. degree (outstanding distinction) with an emphasis in electrical and electronic systems from the University of Antioquia, Colombia, in 2015. He received a B.S. degree in Control Engineering from Universidad Nacional de Colombia in 2008. His background includes working as a lecturer in engineering areas. Currently, he is a researcher at the Secure Next Generation Resilient Systems Lab (SENTRY) of King Abdullah University of

Aminta Naidili Castillo Robles

Ahmad Bin Afzal

Afzal received his B.Tech. degree in Electrical Engineering from Zakir Husain College of Engineering and Technology in 2023. He is an ECE Master's student in the Secure Next Generation Resilient Systems Lab (SENTRY) under the supervision of Professor Charalambos Konstantinou at King Abdullah University of Science and Technology (KAUST). Education Profile Bachelor of Technology in Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, India.

Abdullah Raafat Alfaraj

Abdullah Alfaraj is an undergraduate student at the University of Illinois Urbana-Champaign. He is pursuing a degree in electrical engineering, focusing on semiconductors and control systems. Abdullah joined the Secure Next Generation Resilient Systems Lab (SENTRY) led by Professor Charalambos (Harrys) Konstantinou as a KAUST Gifted Student Program (KGSP) student, appealed by circuit design and analysis.
<> optimization (91)

Integrated Learning and Optimization and Computationally Efficient Model Predictive Control for Power Systems Applications

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This dissertation studies novel model predictive control and integrated learning-optimization methodologies that enable computationally efficient, robust, and scalable real-time control and decision-making in modern power systems, improving renewable microgrid operation, electricity market performance, and optimization under uncertainty.

Imran Pervez

Imran Pervez's research focuses on integrating machine learning, optimization, and advanced control methodologies for power systems, with applications in renewable energy integration, stochastic and robust model predictive control, and data-driven decision-making to improve the efficiency, reliability, and economic performance of modern energy systems.

Differentially Private Training in the LLM Era

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B9 R2325

This seminar explores how to make differentially private training practical and scalable for large language models and new methods that enable stronger privacy guarantees without sacrificing performance.

Shaymaa Mahmoud

M.S. Student, Statistics
Shaymaa Mahmoud is currectly a M.S. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Shaymaa Mahmoud received her B.S. in Data Science with a minor in Computer Science from the American University in Cairo in May 2025, and spent the spring of 2024 studying abroad at Kent State University. During her undergraduate years, she worked across several research groups.She did research internship during her semester abroad at the Advanced Telerobotics Research

Multicarrier Waveform Design and Optimization for Doubly Dispersive Wireless Channels

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This thesis develops a structured approach to multicarrier waveform design for LTV channels to support reliable communication in high-mobility scenarios.

Resilient renewable energy networks designed for the desert

1 min read ·
Accounting for weather extremes is essential for designing reliable renewable energy systems in hot, arid regions.

Fully Decentralized Inference for Spatial Data Using Low-Rank Models

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B9 R2325

This talk introduces a novel, fully decentralized optimization framework to enable scalable parameter inference in large spatial low-rank models, supported by both theoretical guarantees and empirical validation.

Approximation and Optimization for Neural Networks

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In this talk, we consider new connections between the approximation and optimization of neural networks. Instead of relying on excessive over-parametrization to achieve zero training loss, we identify good minima by comparison with established approximation bounds.

A Dichotomy of Continuous Finite Element Spaces and Its Application to Energy-Conservative Galerkin Methods for Nonlinear and Dispersive Wave Equations

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B1 L3 R3119

This talk illustrates why energy-conservative Galerkin methods for nonlinear and dispersive wave equations achieve optimal convergence rate with odd-degree polynomials.

Accelerating Branch-and-Bound Graph Algorithms with GPUs

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B9 L2 R2325

This talk presents multiple techniques that we have developed to load balance the search tree traversal on GPUs and mitigate the strain on memory capacity and bandwidth.

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Mathematical Imaging in the Era of AI

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B1 L23 R3119

In this talk, I will present mathematical imaging as a meeting point of classical analysis and modern AI. I will highlight how ideas from optimisation and mathematical modelling can guide the development of structure-preserving deep learning methods, offering new, principled approaches to large-scale inverse imaging problems.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

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B4/5 L0 A0215

This thesis introduces a novel framework for asynchronous first-order stochastic optimization centered on heterogeneous worker speeds that remain fast, stable, and even provably optimal.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

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B9 L2 R2325

This talk will discuss how to design asynchronous optimization methods that remain fast, stable, and even provably optimal.

Towards Flexible Polyhedral Nets

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B4 L5 R5209

This thesis research provides a comprehensive classification of flexible geometric nets of arbitrary size in both Euclidean and isotropic geometries, revealing that only two distinct classes exist in each setting and demonstrating their relationship to mechanical design.

Training Neural Networks at Any Scale

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B9 L2 R2325

The talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning, spanning domains from vision to language models.
KAUST-CEMSE-CS-Graduate-Seminar-Athman-Bouguettaya-Service-Based-Approach-Drone-Service-Delivery-Skyway-Networks.jpg

A Service-Based Approach to Drone Service Delivery in Skyway Networks

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B9 L2 R2325

This talk presents a novel service framework that optimizes drone package delivery by composing the best services based on payload, time, and cost while considering environmental factors for both single drones and swarms.

BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training

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B9 L2 R2325

We introduce BiCoLoR, the first algorithm to combine local training with bidirectional compression using arbitrary unbiased compressors, achieving accelerated complexity and demonstrating superior empirical performance.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Randomized Greedy Algorithms for Neural Network Optimization in Solving Partial Differential Equations

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B4 L5 R5220

This thesis introduces the randomized orthogonal greedy algorithm (ROGA) to bridge the gap between theoretical and practical performance of shallow neural networks for solving partial differential equations by overcoming key optimization challenges to achieve provably optimal convergence rates.
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Chaabane Mankai

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Conformal Mapping and Invariants – Motivating Numerical PDE Approaches

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B1 L3 R3119

This study explores conformal mappings and invariants, particularly capacity and hyperbolic length, using PDE-based numerical methods (hp-FEM and BIE) to solve geometric optimization problems, such as minimizing heat loss or maximizing it under distance constraints, for domains with complex geometries.

Convergence of Descent Methods under Polyak- Lojasiewicz-Kurdyka Properties

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B1 L3 R3119

This talk presents a novel convergence analysis for a class of descent methods in nonsmooth and nonconvex optimization under the Polyak-Lojasiewicz-Kurdyka properties, proving finite termination under certain conditions and revealing incompatibilities between these properties and gradient Lipschitz continuity in certain frameworks.

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

KAUST Research Workshop on Optimization and Big Data

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B19 L3 H2

The age of "big data" is here: data of unprecedented sizes is becoming ubiquitous, which brings new challenges and new opportunities. With this comes the need to solve optimization problems of unprecedented sizes.

Everything you always wanted to know about single machine large-scale graph analytics

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B9 L2 H1

Abstract: Graphs are a natural way to capture relations among objects or people. This has led to graph processing systems being used in a wide variety of fields, ranging from biology to social networks, and a large number of such systems have been described in the recent literature. The focus of our recent work is on single machine graph processing systems where the graph is processed from main memory or external storage. We perform a systematic comparison of various techniques proposed to speed up in-memory multicore graph processing. In addition, we take an end- to-end view of execution time

Estimating the expected information gain in Bayesian optimal experimental design

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B9 L2 H1

Optimal experimental design for parameter estimation is a fast-growing area of research. Let us consider the experimental goal to be the inference of some attributes of a complex system using measurement data of some chosen system responses, and the optimal designs are those that maximize the value of measurement data. The value of data is quantified by the expected information gain utility, which measures the informativeness of an experiment. Often, a mathematical model is used that approximates the relationship between the system responses and the model parameters acting as proxies for the attributes of interest.

Spectrum Management for Future Generations of Cellular Networks

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B1 L2 R2202

The demand for wireless communication is ceaselessly increasing in terms of the number of subscribers and services. Future generations of cellular networks are expected to allow not only humans but also machines to be immersively connected. However, the radio frequency spectrum is already fully allocated. Therefore, developing techniques to increase spectrum efficiency has become necessary. In that context, this dissertation analyzes two spectrum sharing techniques that enable efficient utilization of the available radio resources in cellular networks. The first technique, called full-duplex (FD) communication, uses the same spectrum to transmit and receive simultaneously. The second spectrum sharing technique, called non-orthogonal multiple access (NOMA), allows a transmitter to communicate with multiple receivers through the same frequency-time resource unit.

Virtual sensing: methods and applications

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B9 L2 H1 R2322

Abstract Due to the inherent complexity, physical or economical constraints, a number of important physical and biological systems are inaccessible. Only few, often indirect, experimental data are available to extract information or to manipulate and control the system. Introduced as a promising technology to respond to the growing need for reliable, accurate, fast, robust and low-cost sensing strategies, virtual sensors are gaining importance in several fields. These sensors combine the available data with effective estimation methods and algorithms to access non-measurable variables of

Use Mathematics and get a SPARQL Query Engine (almost) for Free! It’s MAGiQ!

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B9 L2 H1 R2322

Existing RDF engines are designed for specific hardware architectures; porting to a different architecture (e.g., GPUs) entails enormous implementation effort. We explore sparse matrix algebra as an alternative for designing a portable, scalable and efficient RDF engine.

Langevin Monte Carlo as an optimization algorithm

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B9 L2 H1 R2322

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Dynamics and Convergence of Weight Normalization for Training Neural Networks

Guido Montufar, Assistant Professor, Departments of Mathematics and Statistics, University of California, Los Angeles (UCLA)

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B1 L3 R3119

We present a result on the convergence of weight normalized training of artificial neural networks. In the analysis, we consider over-parameterized 2-layer networks with rectified linear units (ReLUs) initialized at random and trained with batch gradient descent and a fixed step size. The proof builds on recent theoretical works that bound the trajectory of parameters from their initialization and monitor the network predictions via the evolution of a ''neural tangent kernel'' (Jacot et al. 2018). We discover that training with weight normalization decomposes such a kernel via the so called ''length-direction decoupling''. This in turn leads to two convergence regimes. From the modified convergence we make a few curious observations including a natural form of ''lazy training'' where the direction of each weight vector remains stationary.

Space-Time Tomographic Reconstruction of Deforming Objects

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B1 L2 R2202

In this thesis, a variety of applications in computer vision and graphics of inverse problems using tomographic imaging modalities will be presented: (i) The first application focuses on the CT reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. (ii) The second application is about space-time tomography (iii) Base on the second application, the third one is aiming to improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations, by a warp-and-project strategy. Finally, with a physically plausible divergence-free prior for motion estimation, as well as a novel view synthesis technique, we present applications to dynamic fluid imaging which further demonstrates the flexibility of our optimization frameworks

Understanding a Block of Layers in Deep Neural Networks: Optimization, Probabilistic and Tropical Geometric Perspectives

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KAUST

In this dissertation, we aim at theoretically studying and analyzing deep learning models. Since deep models substantially vary in their shapes and sizes, in this dissertation, we restrict our work to a single fundamental block of layers that is common in almost all architectures. The block of layers of interest is the composition of an affine layer followed by a nonlinear activation function and then lastly followed by another affine layer. We study this block of layers from three different perspectives. (i) An Optimization Perspective. We try addressing the following question: Is it possible that the output of the forward pass through the block of layers highlighted above is an optimal solution to a certain convex optimization problem? As a result, we show an equivalency between the forward pass through this block of layers and a single iteration of certain types of deterministic and stochastic algorithms solving a particular class of tensor formulated convex optimization problems.

Quantitative understanding of transcription factor regulation at network- and molecular-levels through optimization and deep learning

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KAUST

Transcription factors are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. Transcription factor regulation is thus fundamental to understanding not only the system-level behaviors of gene regulatory networks, but also the molecular mechanisms underpinning endogenous gene regulation. In this talk, I will introduce our efforts on developing novel optimization and deep learning methods to quantitatively understanding transcription factor regulation at network- and molecular-levels. Specifically, I will talk about how we estimate the kinetic parameters from sparse time-series readout of gene circuit models, and how we model the relationship between the transcription factor binding sites and their binding affinities.

Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach

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KAUST

In this work, we develop a new framework of trajectory planning for AUVs in realistic ocean scenarios. We divide this work into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. The latter are used to specify both the ocean current and the bathymetry. We apply a NLP to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we address uncertainty in the ocean current field. The uncertainty in the current is described in terms of a finite ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. Advanced visualization tools are used to amplify simulation results.

Stochastic Optimization in Target Positioning and Location-based Applications

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KAUST

This event has been postponed from 20th July to 27th July. Stochastic optimization refers to the minimization/maximization of an objective function in the presence of randomness. The randomness may appear in objective functions, constraints, or optimization methods. It has the advantage of dealing with uncertainties that deterministic optimizers cannot solve or cannot solve efficiently. In this work, we discuss the implementation of stochastic optimization methods in solving target positioning problems and tackling key issues in location-based applications.

Optimal Algorithms for Affinely Constrained, Distributed, Decentralized, Minimax, and High-Order Optimization Problems

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B5 R5220

In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.

Powering the bottom line

1 min read ·
A risk-based optimization scheme boosts confidence and profitability for future mixed-technology power plants.