Prof. Marc Genton
Marc G. Genton received the Ph.D. degree in Statistics (1996) from the Swiss Federal Institute of Technology (EPFL), Lausanne. He is a Distinguished Professor of Statistics at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. He is a fellow of the American Statistical Association, of the Institute of Mathematical Statistics, and the American Association for the Advancement of Science, and is an elected member of the International Statistical Institute. In 2010, he received the El-Shaarawi award for excellence from the International Environmetrics Society and the Distinguished Achievement award from the Section on Statistics and the Environment of the American Statistical Association. He received and ISI Service award in 2019 and the Georges Matheron Lectureship award in 2020 from the International Association for Mathematical Geosciences. His research interests include statistical analysis, flexible modeling, prediction, and uncertainty quantification of spatio-temporal data, with applications in environmental and climate science, renewable energies, geophysics, and marine science.
Dr. Huang Huang
Huang is a Research Scientist in the Spatio-Temporal Statistics & Data Science group at King Abdullah University of Science and Technology (KAUST). Before working at KAUST, he did research on statistical computing for climate applications as a postdoc at the National Center for Atmospheric Research (NCAR), the Statistical and Applied Mathematical Sciences Institute (SAMSI), and Duke University. He received his Ph.D. in Statistics in 2017 from KAUST, master’s and bachelor’s degree in Mathematics in 2014 and 2011 from Fudan University. His research interests include spatio-temporal statistics, functional data analysis, Bayesian modeling, machine learning, and high-performance computing for large datasets.
Dr. Sameh Abdulah
Sameh is Research Scientist at the Extreme Computing Research Center, King Abdullah University of Science and Technology, Saudi Arabia. Sameh received his M.S. and Ph.D. degrees from Ohio State University, Columbus, USA, in 2014 and 2016. His work is centered around High-Performance Computing (HPC) applications, data management in big data, large spatial datasets, parallel statistical applications, algorithm-based fault tolerance, and Machine Learning and Data Mining algorithms.
Spatial data science is concerned with analyzing the spatial distributions, patterns, and relationships of data over a predefined geographical region. It relies on the dependence of observations where the primary assumption is that nearby spatial values are associated in a certain way. For decades, the size of most spatial datasets was modest enough to be handled by exact inference. Nowadays, with the explosive increase of data volumes, High-Performance Computing (HPC) has become a popular tool for many spatial applications to handle massive datasets. Big data processing becomes feasible with the availability of parallel processing hardware systems such as shared and distributed memory, multiprocessors and GPU accelerators. In spatial statistics, parallel and distributed computing can alleviate the computational and memory restrictions in large-scale Gaussian random process inference. In this course, we will first briefly cover the motivation, history, and recent developments of statistical methods so that the students can have a general overview of spatial statistics. Then, the cutting-edge HPC techniques and their application in solving large-scale spatial problems with the new software ExaGeoStat will be presented.
Statisticians with interests in High-Performance Computing and large-scale Spatial Statistics.
- Spatial statistics overview (1 hour, Marc & Huang)
- High-Performance Computing (HPC) overview (1 hour, Sameh)
- Large-scale spatial statistics using R packages with Code Examples (1 hour, Huang & Sameh)