Profiles

Al-Khawarizmi Distinguished Professor of the KAUST Statistics Program, Marc G. Genton, is a specialist in spatial and spatio-temporal statistics with environmental applications. His work has revolutionized environmental data science, addressing large-scale problems involving spatial and temporal datasets. To emulate climate model outputs of more than one billion temperature data points, he developed 3-D space-time stochastic generators using spectral methods and fast Fourier transforms.

Genton is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, the American Association for the Advancement of Science, and an elected member of the International Statistical Institute (ISI).

In 2010, he received the El-Shaarawi Award for Excellence from the International Environmetrics Society (TIES) and the Distinguished Achievement Award from the Section on Statistics and the Environment (ENVR) of the American Statistical Association (ASA). In 2017, he was honored with the Wilcoxon Award for Best Applications Paper in Technometrics. He received an ISI Service Award in 2019 and the Georges Matheron Lectureship Award in 2020 from the International Association for Mathematical Geosciences (IAMG).

He led a Gordon Bell Prize finalist team with the ExaGeoStat software at Supercomputing 2022. In 2023, he was awarded the Royal Statistical Society’s (RSS) Barnett Award for his outstanding contributions to environmental statistics. He also received the prestigious 2024 Don Owen Award from the San Antonio Chapter of the American Statistical Association and led a Gordon Bell Prize finalist team in Climate Modeling for the Exascale Climate Emulator at SC24.

In addition to authoring over 300 publications, Genton has edited a book on skew-elliptical distributions and their applications. He has given more than 400 presentations at conferences and universities worldwide.

Genton received his Ph.D. in statistics in 1996 from the Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland. He also holds an M.S. degree in applied mathematics teaching, earned in 1994 from EPFL.

Before joining KAUST, he held prominent faculty positions at the Massachusetts Institute of Technology (MIT), North Carolina State University, the University of Geneva and Texas A&M University.

Professor Genton’s research centers around spatial and spatio-temporal statistics, including the statistical analysis, visualization, modeling, prediction and uncertainty quantification of spatio-temporal data. A wide range of applications can be found in environmental and climate science, renewable energies, geophysics and marine science.

Currently, he is developing high-performance computing tools for spatial statistics and expanding the capabilities of ExaGeoStat, the software developed by his Spatio-Temporal Statistics and Data Science (STSDS) research group and the Extreme Computing Research Center (ECRC).

An in-depth, five-year study of wind energy potential in Saudi Arabia, led by Genton, culminated in a comprehensive plan for developing the Kingdom's future wind energy strategy. With the help of apps and 3-D glasses, he has also demonstrated how virtual reality can help visualize environmental data on smartphones.

Dr. Sameh Abdulah is an HPC research scientist specializing in high-performance computing (HPC), and large-scale data analytics. He is a 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.

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.