Profiles

Dr. Harrou received an M.S. degree in Telecommunications and Networking from the University of Paris VI and a Ph.D. degree in Systems Optimization and Security from the University of 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 Senior Research Scientist with the Division of Computer, Electrical, and Mathematical Sciences and Engineering at King Abdullah University of Science and Technology. He is a co-author of "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications" (Elsevier, 2020) and "Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning" books. And he is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). Harrou is the author of more than 250 refereed journal articles, conference publications, and book chapters. He received two IEEE ECBIOS 2021 Best Paper Awards. Dr. Harrou is featured in Stanford University’s list of the world’s Top 2% scientists for 2020-2025. And he is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).

Dr. Harrou’s research interests are in the area of statistical anomaly detection and process monitoring with a particular emphasis on data-driven methods. The algorithms developed in Dr. Harrou’s research are utilized in many applications to improve the operation of various environmental, chemical, and electrical systems.

Dr. Sameh Abdulah is an HPC senior research scientist specializing in high-performance computing (HPC), and large-scale data analytics. He is a Senior Research Scientist at the Computer, Electrical and Mathematical Sciences and Engineering Division at KAUST. His work focuses on developing scalable algorithms and efficient software frameworks to address complex computational challenges across diverse scientific and engineering domains, including spatial statistics.

He serves as a key link between three major research groups within the extreme computing research at KAUST: the Hierarchical Computations on Manycore Architectures (HiCMA) group led by Professor David Keyes, the Spatio-Temporal Statistics & Data Science (STSDS) group led by Professor Marc Genton, and the Environmental Statistics (ES) group led by Professor Ying Sun. His primary role is to bridge advanced parallel linear algebra (LA) innovations with high-performance computing (HPC) in the spatial statistics field in the context of climate and weather applications.

Dr. Abdulah was honored with the ACM Gordon Bell Prize for Climate Modelling in November 2024. His team's pioneering work in climate simulation set new benchmarks in computational efficiency and resolution, transforming how climate data is modeled and analyzed. He was also part of the KAUST team nominated for the ACM Gordon Bell Prize in the general track for spatial data modeling/prediction in 2022.

He has significantly contributed to scalable matrix computations, particularly in designing numerical libraries that leverage modern hardware architectures. His expertise includes mixed-precision matrix computations, geostatistical modeling, and prediction. He has also developed cutting-edge methodologies for accelerating data-intensive simulations, enabling transformative weather/climate modeling advancements.

As a passionate advocate for open-source software, Dr. Abdulah is actively involved in collaborative research and software development, sharing tools and libraries that empower researchers globally. His work is driven by a commitment to innovation and interdisciplinary collaboration, harnessing the power of HPC to tackle some of the most pressing challenges in computational science.

Sameh's research includes HPC, large-scale spatial and spatio-temporal statistics, GPU acceleration, and mixed-precision and low-rank algorithms for AI-enabled scientific applications, with a particular focus on climate and environmental modeling. He is a core developer of ExaGeoStat, an open-source exascale geostatistical software framework widely used by the community for large-scale spatial modeling and benchmarking on modern supercomputers.

Adding the HPC capabilities to existing science is a big challenge. Statistics has a huge number of tools and methods that can be more attractive if they scaled up. Dr Abdulah is doing this by working through two different groups to transfer knowledge and experience between two different views of the same problem. In other words, he is moving the traditional statistical tools and methods to the HPC era.

Dr. López Oriona received his bachelor’s degree in Mathematics and his master’s degree in Statistics both from the University of Santiago de Compostela (Spain), his master’s degree in Big Data Analytics from the European University of Madrid (Spain), and his PhD in Statistics from the University of A Coruña (Spain). He was a Visiting Researcher at the Sapienza University of Rome (Italy), the University of Sydney (Australia), the Helmut Schmidt University of Hamburg (Germany), and Lancaster University (United Kingdom). Since September 2023, he is a Postdoctoral Fellow in the Environmental Statistics Research Group at KAUST, under the supervision of Professor Ying Sun.

Dr. López Oriona research interests include Computational Statistics, Directional Statistics, Fuzzy Set Theory, and Time Series Data Mining, among others.

Qilong Pan is a Ph.D. candidate in Statistics at KAUST, supervised by Prof. Ying Sun. His research focuses on scalable Gaussian Process modeling, high-performance statistical computing, and GPU-accelerated inference for large-scale spatial and computer experiment data.

Qilong Pan's work bridges statistical modeling, optimization, and high-performance computing (HPC) to tackle complex challenges in geospatial analytics and machine learning. He develops efficient algorithms and software for Vecchia-based approximations, aiming to enable practical Gaussian Process applications on modern supercomputers.

Ziling is a doctoral student in Statistics at King Abdullah University of Science and Technology (KAUST), where she is conducting her research under the mentorship of Professor Hernando Ombao in the Biostatistics research group and Professor Ying Sun in the Environmental Statistics research group. Ziling Ma earned her Bachelor's degree in Mathematics and Applied Mathematics from Tianjin Normal University in China. She then furthered her education at KU Leuven in Belgium, where she was awarded a Master's degree in Mathematics. Ziling embarked on her Ph.D. journey in Statistics in August 2023.

Ziling Ma's research interests are situated at the crossroads of time series analysis, robust statistics, and functional data analysis.