statistics

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.

Brain waves in boxes

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A statistical method helps to identify abnormal signals in electroencephalograms and locate their source in the brain.

2015 Workshop on Computational Space-Time Statistics

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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

A clearer view of rainfall patterns

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Improved random modeling allows scientists to generate realistic patterns of high-frequency rainfall.

2016 Workshop Statistics for High-Dimensional & Complex Data

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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

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A statistics-driven method for the early detection of emerging problems in industrial processes could improve industrial safety, reliability and productivity.

A safe sweep of weather data

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A statistical technique for automatically cleaning erroneous data from weather-balloon observations will improve the accuracy of weather forecasting.

Keeping antennas at peak performance

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Sensitive detection of partial faults in antenna systems could prevent performance degradation in wireless networks.

Smart healthcare uses stats to spot a stumble

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Statistical monitoring technology can detect serious falls and immediately warn healthcare providers.

Professor Sun receives the Environment Early Investigator Award 2017

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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

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​​​​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

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A statistical technique for modeling large datasets improves interpretation of climate and environmental data.

2017 Workshop Modern Statistics for Complex Data Structures

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2017 Workshop Modern Statistics for Complex Data Structures

One bad apple can spoil a robot swarm

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Coordinated robot swarms can achieve amazing feats, but even a single faulty unit can have serious consequences.

Ph.D. Student Gaurav Agarwal received an Honorable Mention from ENVR Student Paper Competition

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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."

Revealing hidden relationships in data

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A method to visualize hidden statistical structure helps make sense of environmental data.

Making data more accessible

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Ying Sun is a multi-award-winning statistician who is inspired by the value of statistics in solving real-world problems.

Ph.D. Student Yuxiao Li won the ENAR Distinguished Student Paper Award

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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

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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

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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."

New stats apps show a virtual reality

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Harnessing the power of virtual reality will help to visualize data and improve statistical models.

Postdoc Carolina Euan received Sylvia Esterby Presentation Award from TIES

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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."

Clustering helps unlock secrets of the human brain

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The latest statistical methods from research on complex high-dimensional environmental data also yield powerful tools for interpreting brain activity.

Taking aim at outliers

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Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.

Honing in on hotspots in spatial data

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More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

3 Faculty Positions in Statistics 2020 at KAUST

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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).

2019 Statistics and Data Science Workshop

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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

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Factors influencing the tolerance of barley to saline soils have been uncovered using an advanced robust statistical technique.

Pop stats for big geodata

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A universal high-performance computing interface allows popular statistical tools to run efficiently on large geospatial datasets.

KAUST Environmental Statistics group publishes book exploring multivariate statistical analysis

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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.

Today we celebrate World Statistics Day!

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Let’s discover the world of the statisticians at KAUST and be ready to be amazed by their cutting-edge research!

Deep learning in the emergency department

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

KAUST STAT students come together for second ASA chapter meeting

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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

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A high-frequency model developed using data from new high-precision rain gauges gives fresh insight into the dynamics of rain and runoff events.

Mixing precision for model acceleration

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A mixed-precision approach for modeling large geospatial datasets can achieve benchmark accuracy with a fraction of the computational run time.

Trio of tuning tools for modeling large spatial datasets

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Statistical tools help fine-tune the parameters and approximations of models used to make sense of large spatial datasets.

Deeper defense against cyber attacks

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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.

Can machine learning help predict disease spread?

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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.

Zuzheng Wang

Ph.D. Student, Statistics

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

Qilong Pan

Ph.D. Student, Statistics
Qilong Pan is a MS/Ph.D. student in the Environmental Statistics group under the supervision of Prof. Ying Sun at the King Abdullah University of Science and Technology (KAUST). He believes that life and study at KAUST would be an unforgettable experience for his whole life. Education and early career B.S. in Statistics in 2021, Wuhan University of Technolgy B.A. in English in 2021, Huazhong University of Science and Technology Research Interests Environmental statistics, Spatio-temporal statistics, Deep Learning

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Fouzi Harrou

Research Scientist, Statistics
Dr. Harrou received an M.Sc. degree in telecommunications and networking from the University of Paris VI and the Ph.D. degree in systems optimization and security from the University Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He is currently a Research Scientist with the Division of Computer, Electrical, and