Extreme Events and Risk Analysis Research Extreme events research at KAUST focuses on the study of rare, high-impact occurrences that significantly disrupt the norm, such as natural disasters, financial crises and extreme weather events. Research Focus Research in this field leverages computational and statistical methodologies to understand the underlying mechanisms of these phenomena and develop predictive models to anticipate their occurrence and mitigate their risk, involving fields like climatology, finance and engineering.
Health and Environmental Applications Research The CEMSE STAT program extends its expertise in statistical methodologies to a variety of critical domains. Our interdisciplinary research efforts are aimed at advancing knowledge and addressing pressing issues. Research Focus Biostatistics: involves applying statistical methods to biological and medical research. This research area focuses on designing studies, analyzing data, and interpreting results to advance knowledge in health, medicine, and biology. Public Health and Epidemiology: focuses on studying the distribution and determinants of health and diseases in populations. It aims to
Research Areas Research Developing Data-Driven Solutions for a Better World At KAUST, our Statistics program is a dynamic hub of research excellence, pushing the boundaries of statistical theory and its application to real-world challenges. Our world-renowned faculty lead diverse research groups, fostering collaboration and innovation to drive data-driven solutions in critical areas like healthcare, environmental sustainability, and beyond. Our research is defined by: Methodological Innovation: We constantly push the boundaries of statistical theory and computational techniques to extract knowledge from the most
Statistical Methodology Research The CEMSE STAT program fosters a vibrant research environment dedicated to pushing the boundaries of statistical methodologies through cutting-edge research. Research Focus Computational Statistics: Focuses on using computational methods to analyze and interpret statistical data. This research area combines statistics and computer science to handle large datasets and complex models, which are crucial for fields like machine learning, bioinformatics and data mining. Bayesian Deep Learning: Merges Bayesian statistics with deep learning to enhance neural network models with uncertainty estimation
