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.

Meet the Team

Get to know STSDS team

SINAPE Talk

Large-Scale Spatial Data Science with ExaGeoStat

Events

Learn more about STSDS events

ExaGeoStat

Marc Genton Teaching

Talks from STSDS@KAUST members!

Our research activities include the following topics:

  • Spatio-temporal statistics
  • Data science
  • Spatial extremes
  • Geostatistics for large datasets
  • Non-Gaussian random fields
  • Copulas
  • Multivariate spatial statistics
  • Nonstationary models
  • Wind and solar power forecasting
  • Multivariate data analysis
  • Data mining and machine learning
  • Visualization of functional and image data
  • Skew-elliptical distributions
  • Robustness
  • Data assimilation

MPCR: Highly Optimized Multi-Precision Package in R

  • MPCR main repo (v1.1.0, November 5th, 2023): click here

ExaGeoStat: Geostatistics at Exascale!

KAUST Competitions on Spatial Statistics for Large Datasets

2023 KAUST Competition on Spatial Statistics for Large Datasets

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

2022 KAUST Competition on Spatial Statistics for Large Datasets

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

2021 KAUST Competition on Spatial Statistics for Large Datasets

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

Student in Focus

Watch STAT Student in Focus Jian Cao on YouTube.
Watch STAT Student in Focus - Mary Lai Salvana on YouTube.
Watch STAT Student in Focus Wanfang Chen on YouTube.
Watch STAT Student in Focus - Zhuo Qu on YouTube.

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