Research Groups
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The Bayesian Computational Statistics and Modeling Research Group, led by Professor Håvard Rue, focuses on computational Bayesian statistics and methodology, developing practical tools like R-INLA for approximate Bayesian analysis of latent Gaussian models, with applications including spatial statistics using stochastic partial differential equations.
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Professor Filippone's current research interests include the development of tractable and scalable Bayesian inference techniques for Gaussian processes and Deep Learning models, with applications in life and environmental sciences.
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The KAUST Biostatistics Group develops novel statistical methods and models for biological processes with complex dependence structures.
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Led by Prof. Ying Sun, the Environmental Statistics research group is dedicated to developing statistical models and methods for space-time data to solve important environmental problems. We focus on statistical inferences of spatio-temporal processes, including developing informative graphical tools for complex space-time datasets, building realistic models for natural spatio-temporal processes and finding computationally efficient methods for estimating and assessing the fit of such models.
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Led by Prof. Raphaël Huser, the Extreme Statistics (XSTAT) research group develops specialized statistical models for low-probability, high-impact extreme events. These models are designed for effective tail extrapolation and the assessment of future, potentially more extreme, risks.
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Led by Prof. Paula Moraga, the GeoHealth research group is primarily focused on the development of frontier geospatial methods and computational tools to solve challenging problems in public health and make a positive impact on the world.
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Led by Prof. Marc Genton, the Spatio-Temporal Statistics and Data Science (STSDS) research group focuses 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.
Spatio-Temporal Statistics and Data Science (STSDS)
machine learning spatio-temporal statistics data science Spatial extremes geostatistics large datasets non-Gaussian random fields copulas multivariate spatial statistics forecasting solar power wind power multivariate analysis data analysis visualization skew-elliptical distributions Robustness data assimilation data mining
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Led by Prof. David Bolin, the Stochastic Processes and Applied Statistics group develops methodology for statistical models involving stochastic processes and random fields. A main focus is the development of statistical methods based on stochastic partial differential equations.