2021 KAUST Competition on Spatial Statistics for Large Datasets Competitions KAUST Competition on Spatial Statistics Introduction With the development of observing techniques and computing devices, it has become easier and more common to obtain large datasets. Statistical inference in spatial statistics becomes computationally challenging. For decades, various approximation methods have been proposed to model and analyze large-scale spatial data when the exact computation is infeasible. However, in the literature, the performance of the statistical inference using those proposed approximation methods was usually assessed with small and medium datasets only, for which the exact solution can be obtained. Then
2022 KAUST Competition on Spatial Statistics for Large Datasets Competitions KAUST Competition on Spatial Statistics Introduction Spatial statistics is an example of a scientific field that requires novel methods to model and analysis large-scale spatial data. In the literature, research studies have proposed different approximation methods to handle large data sizes on traditional hardware. However, with the availability of modern High-Performance Computing (HPC) systems, large-scale exact computation becomes possible and allows processing larger data sizes more easily than before. For decades, the lack of large-scale exact computation has led to an inefficient assessment of spatial modeling approximation
2023 KAUST Competition on Spatial Statistics for Large Datasets Competitions KAUST Competition on Spatial Statistics Introduction The rapid increase in the volume of geospatial data over recent years has added more challenges to processing these data using traditional methods. Thus, geospatial applications have brought High-Performance Computing (HPC) into the mainstream and further increased its use in the spatial statistics field. ExaGeoStat is one example of an HPC software that enables large-scale parallel generation, modeling, and prediction of large geospatial data via covariance matrices. Unlike other existing tools, which typically rely on approximations to deal with the vast data volume on day-use
ExaGeoStat Research ExaGeoStat enables statisticians to tackle computationally challenging scientific problems at large-scale, while abstracting the hardware complexity, through state-of-the-art high-performance linear algebra software libraries.
Marc Genton Publications Research Book Genton, M. G. (2004), Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality, Edited Volume, Chapman & Hall / CRC, Boca Raton, FL, 416 pp. Surface Boxplots (zipped file) 2024 Hu, Z., Tong, T., and Genton, M. G. (2024), "A pairwise Hotelling method for testing high-dimensional mean vectors,"Statistica Sinica, 34, 229-256. Mondal, S., Arellano-Valle, R. B., and Genton, M. G. (2024), "The multivariate modified skew-normal distribution,"Statistical Papers, 65, 511-555. Mondal, S., and Genton, M. G. (2024), "A multivariate skew-normal-Tukey-h
Marc Genton Talks Research Genton, M. G. (2023), "Exascale Geostatistics for Environmental Data Science," Workshop, Lancaster, UK. Genton, M. G. (2023), "Learn from my mistakes: career advice from highly successful statisticians," Lancaster University, Lancaster, UK. Genton, M. G. (2023), "Test and Visualization of Covariance Properties for Multivariate Spatio-Temporal Random Fields," Lancaster University, Lancaster, UK. Genton, M. G. (2023), "Learn from my mistakes: career advice from highly successful statisticians," RSS, Harrogate, UK. Genton, M. G. (2023), "Barnett Lecture: Exascale Geostatistics for Environmental
STSDS Servers Research Currently, STSDS has two servers with GPU support to experiment and test codes and report results Name FQDN IP Address CPU Model # sockets # cores per socket # threads per core RAM GPU CPU code name Rooster rooster.kaust.edu.sa 10.68.170.148 Intel(R) Xeon(R) Gold 6230 CPU @ 2.10GHz 2 20 1 256 1x Quadro GV100 Cascade Lake Hen hen.kaust.edu.sa 10.67.24.37 Intel(R) Xeon(R) Gold 6230 CPU @ 2.10GHz 2 20 1 256 1x Quadro GV100 Cascade Lake In case of facing any problems, please email sameh.abdulah@kaust.edu.sa.
Spatio-Temporal Statistics and Data Science Research Group Info Welcome to the Spatio-Temporal Statistics and Data Science Research Group The group of Marc G. Genton, Al-Khawarizmi Distinguished Professor of Statistics, works on the statistical analysis, modeling, prediction, and uncertainty quantification of spatio-temporal data, with applications in environmental and climate science, renewable energies, geophysics, and marine science. Please follow the links below to learn more about the currently ongoing projects and opportunities within the team. We are always looking for talented and hard-working students and postdocs. Talks from STSDS@KAUST members
Marc Genton Teaching Teaching STAT 370 Spring 2023: STAT 370, Spatial Statistics Enrolled students can access course material through KAUST's Blackboard via http://blackboard.kaust.edu.sa Spring 2022: STAT 330, Multivariate Statistics Enrolled students can access course material through KAUST's Blackboard via http://blackboard.kaust.edu.sa Short course Large-Scale Spatial Data Science With ExaGeoStat -- Part1 ( slides/ video), Part2 ( slides/ video), and Part3 ( slides/ video) 2023 Barnett Lecture Exascale Geostatistics for Environmental Data Science ( slides)
