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extstat

Extreme Statistics Research Group

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Interested in Joining the Extreme Statistics (extSTAT) Research Group?

We are currently accepting applications for Fall 2021.

Apply to KAUST

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about research group

Prof​​. Raphael Huser's group focuses on two aspects of extreme statistics:

  1. Development of statistical models and inference methods for extreme events. Although the methodology might be applied in a wide range of contexts, we are mainly interested in the modeling of complex environmental extremes, such as floods, heat waves, wind speeds, etc. Important questions include the characterization of space-time dependence at extreme levels, the modeling and estimation of non-stationarity (in space and time), and the development of efficient inference methods to fit these extremal models to real data.​

  2. Development of efficien​t methods for large datasets. These include the development of inference a​pproaches for complicated extremal models (e.g., composite likelihoods, local likelihoods, etc.), the joint modeling of multiple parallel time series, or the spatial prediction for large datasets.

 

Latest News
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Top honors for KAUST extSTAT group at EVA 2023

Top honors for KAUST extSTAT group at EVA 2023

2023.09.13
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Daniela Cisneros

Congratulations to Daniela for successfully defending her PhD thesis

2023.09.11
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Wildfire_Greece

New paper accepted in Artificial Intelligence for the Earth Systems (AIES)

2023.09.10
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RoyalStatisticalSociaty_SeriesC_ExtSTAT

New paper accepted in Journal of the Royal Statistical Society: Series C

2023.08.05
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ASA_AS

New paper accepted in The American Statistician

2023.07.31Bayes estimators
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Yalla_ExtSTAT

Congratulations to Paolo and Yalla team for their success at the EVA2023 conference

2023.07.06
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KAUST-CEMSE-Stat Students News

KAUST STAT Program graduates secure faculty positions at prominent U.S. universities

2023.04.19
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matheus_pic_thobe

Congratulations to Matheus for successfully defending his PhD thesis

2023.04.04
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Meet Our People
KAUST-CEMSE-STAT-ExtSTAT-Raphael-Huser
It is often low-probability extreme events, rather than averages, that lead to the biggest impacts. In the Extreme Statistics Research Group, we develop novel statistical methodology and machine learning methods to better understand and predict high-impact events in a wide range of applications ranging from climate and earth sciences, to finance, and neuroscience.
Raphaël Huser, Associate Professor, Statistics
KAUST-CEMSE-STAT-ExtSTAT-Xuanjie-Shao
Work hard, play hard.
Xuanjie Shao, PhD Student, Statistics
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All models are wrong, but some are useful.-George Box
Noura Alotaibi, PhD Student, Statistics
KAUST CEMSE Statistics Deepa Pinto
"The best way to find yourself, is to lose yourself in the service of others"
Deepa Pinto, Business Coordinator, Statistics
KAUST-CEMSE-STAT-BIOSTATS-Paolo-Victor-Redondo-Profile-Picture
Research means that you don’t know, but are willing to find out.
- Charles F. Kettering
Paolo Victor Redondo, PhD Student, Statistics
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