geospatial statistics

Scalable Methods for Multivariate Normal Probability Estimation with Applications in Confidence Region Detection, Transport Phenomena, and Parallel Computing Using RCOMPSs

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B2 R5209

This thesis addresses computing high-dimensional multivariate normal (MVN) probabilities in environmental and geospatial applications by combining high-performance computing, numerical approximation, and transport-based covariance modeling to make several important spatial procedures usable at larger scales.

Fully Decentralized Inference for Spatial Data Using Low-Rank Models

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B9 R2325

This talk introduces a novel, fully decentralized optimization framework to enable scalable parameter inference in large spatial low-rank models, supported by both theoretical guarantees and empirical validation.

Jianwei Shi

Postdoctoral Research Fellow, Statistics

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

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B5 R5209

This dissertation develops integrated spatio-temporal forecasting approaches that combine deep learning, climate data, spatial dependencies, and human mobility to improve dengue prediction and support early-warning systems in Brazil.

Geospatial Data Science for Public Health Surveillance

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B4/5 L0 A0215; Zoom Meeting 94713495879

This talk presents an overview of our research on innovative statistical methods and computational tools for geospatial data analysis and health surveillance, and how this work has directly informed strategic policy to reduce disease burden.

Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

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B3 L5 R5220

The statistical modeling of spatial and extreme events provides a framework for the development of techniques and models to describe natural phenomena in a variety of environmental, geoscience, and climate science applications. In a changing climate, various natural hazards, such as wildfires, are believed to have evolved in frequency, size, and spatial extent, although regional responses may vary.

Better health from better data

1 min read ·
The fast-developing science of geospatial statistics offers new insights into diverse health challenges, from cancer hotspots to problems of air pollution and the spread of COVID-19. Three new projects should yield a better understanding of what causes disease in Saudi Arabia and how to fight it. KAUST also stands to make a real contribution to the region by improving health in the Middle East and North Africa (MENA). The field of geospatial statistics and health surveillance, known as GeoHealth, uses data from multiple sources to create disease maps of geographical areas. When information is

Xiran Zhang

Ph.D. Student, Statistics
Xiran Zhang research investigates statistics and high-performance computing, with a particular focus on scalable methods for large-scale geostatistical and spatio-temporal problems.

Xiang Chen

Ph.D. Student, Statistics
Xiang Chen's research focuses on spatio-temporal modeling of climate-sensitive infectious diseases, integrating large-scale health, climate, and human mobility data to improve epidemiological forecasting and support public health decision-making.

Hanan Alahmadi

Ph.D. Student, Statistics

Elias Teixeira Krainski

Research Scientist, Statistics
Elias' research focuses on efficient Bayesian methods for practical implementation, spatio and spatio-temporal statistics, INLA.

Damilya Saduakhas

Ph.D. Student, Statistics