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Leading Statistical Innovation

The Statistics (STAT) Program at KAUST educates students to analyze and model complex real-world problems arising in modern Statistical Data Science. It boasts superb facilities and resources, including access to the latest supercomputing and visualization facilities. Combined with the state-of-the-art experimental laboratories, experimental studies at KAUST are almost unconstrained by physical resources.

Watch Stat Program on YouTube.

Faculty and Groups

Browse our faculty profiles and research groups to explore their expertise, research interests and impactful contributions that drive innovation and discovery at KAUST.

Faculty Profiles

Research Groups

Research

Research at the STAT Program pushes the boundaries of statistical theory and its application to address pressing national and global challenges in healthcare, environmental sustainability and beyond. Key research areas include:

Statistical Methodology
Health and Environmental Applications
Extreme Events and Risk Analysis

The Visiting Student Research Program (VSRP)

The KAUST VSRP is an exciting three-to-six-month research internship opportunity that provides research experience for highly qualified and motivated STEM students to work alongside leading faculty and researchers on impactful projects.

Find a research project that aligns with your study area and apply.

Learn more about VSRP

News

KAUST becomes first FIFA research institute in the Middle East and Asia

1 min read · Tue, Feb 10 2026

News Clip News Press Releases

artificial intelligence generative ai Computer Vision Sports

The right path: New dean Professor Omar Knio focuses on people, purpose, and impact

1 min read · Wed, Jan 28 2026

News Clip News

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read · Wed, Jan 21 2026

Awards News

optimization machine learning communication efficiency artificial intelligence big data algorithm

more

Upcoming Events

KAUST CEMSE STAT Ph.D. Dissertation Defense Xiang Chen Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

Xiang Chen, Ph.D. Student, Statistics
Apr 30, 13:00 - 16:00

B5 R5209

spatio-temporal modeling geospatial statistics infectious disease explainable AI Public Health climate data deep learning computational predictions

more

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Join world-class research on the shores of the Red Sea.

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Statistics (STAT)

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