Professor Marc Genton and former postdoctoral fellow win the 2017 Wilcoxon Award

Marc Genton (pictured), KAUST distinguished professor of statistics, jointly won the 2017 Wilcoxon Award with his former postdoctoral fellow Stefano Castruccio. Photo by Meres J. Weche.

​-By David Murphy, KAUST News

Marc Genton, KAUST distinguished professor of statistics, and his former postdoctoral fellow Stefano Castruccio, have jointly won the 2017 Wilcoxon Award for best applications paper in Technometrics. Technometrics is a journal of statistics for the Physical, Chemical and Engineering Sciences, and is published quarterly by the American Society for Quality and the American Statistical Association. The award announcement was made at the 61st Annual Fall Technical Conference (FTC) in Philadelphia from October 5 to 6, 2017.

Genton and Castruccio's paper was entitled "Compressing an ensemble with statistical models: An algorithm for global 3D spatio-temporal temperature," and appeared in Technometrics, 58, 319-328. Their paper details how, by accounting for the specific statistical structure of global climate simulation data, Genton and Castruccio developed a novel, effective data-compression scheme for large-scale climate simulations. The method discussed promises to significantly reduce data-storage requirements and accelerate the capacity for climate research.

"I am very happy about this award because it is for a statistics paper reporting research that was fully developed at KAUST with Stefano, who's now an assistant professor of statistics at the University of Notre Dame," Genton said.

Current research

Genton, who is based in the University's Computer, Electrical and Mathematical Science and Engineering Division, is also the research leader of the Spatio-Temporal Statistics & Data Science group. The group 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.

"Our group tackles challenging problems in spatio-temporal statistics that arise from modern data science related to climate, environmental and renewable energy sciences," Genton stated.

Genton's current research focus is on spatio-temporal models for big data produced by climate models.

"Specifically, I have a project that investigates space-time statistical models for wind field forecasting with high-performance computing in order to study wind energy resources in Saudi Arabia. This project can be seen as a more challenging extension of the research reported in the paper that won the 2017 Wilcoxon award," he added.

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