Profiles

Hanan Alahmadi is a Ph.D. candidate in the Statistics program (CEMSE) at King Abdullah University of Science and Technology (KAUST), specializing in spatial methods for health surveillance in Saudi Arabia, with a focus on data integration and cluster detection under the supervision of Professor Paula Moraga. Her research combines satellite and health data to monitor disease and environmental risks and to develop GIS platforms that support data-driven decision-making. She holds a master’s degree in statistics from KAUST, where she was supervised by Professor Håvard Rue. Hanan is also a lecturer at King Saud University and the founder of Sorat Alardh, a space-tech startup that harnesses Earth observation data for environmental and health applications. Her work has been recognized with several honors, including a grant from the Communications, Space & Technology Commission (CST) and her selection as a finalist in the Falling Walls competition.

• Geospatial data analysis and health surveillance
• Spatio-temporal disease data
• Statistics and spatial epidemiology
• Geospatial modeling
• Disease mapping

João Vitor Cordeiro de Brito is a MS/PhD student in the Statistics Program at King Abdullah University of Science and Technology (KAUST).

Prior to KAUST, he obtained his degree in Applied Mathematics from the Federal University of Rio de Janeiro (UFRJ), Brazil. During his bachelors, João did undergraduate research on ergodic aspects of problems from number theory, and was awarded with honors/prizes in national and international contests in mathematics.

His research interests lie primarily in the intersection of harmonic analysis, statistical inference, and network science, with applications in neuroscience, among others.

His research interests lie in Bayesian and computational statistics, with a particular focus on the development and application of methods in R-INLA.

Education and Early Career

Paolo Redondo obtained his Bachelor's and Master's (M.Sc.) of Statistics from the University of the Philippines Diliman. He joined the PhD program in Statistics at KAUST in 2021.

• Extreme statistics.
• Time series.

Qilong Pan is a Ph.D. candidate in Statistics at KAUST, supervised by Prof. Ying Sun. His research focuses on scalable Gaussian Process modeling, high-performance statistical computing, and GPU-accelerated inference for large-scale spatial and computer experiment data.

Qilong Pan's work bridges statistical modeling, optimization, and high-performance computing (HPC) to tackle complex challenges in geospatial analytics and machine learning. He develops efficient algorithms and software for Vecchia-based approximations, aiming to enable practical Gaussian Process applications on modern supercomputers.

His research interest is mainly in Bayesian and computational Statistics, currently working on Directional Statistics and applications with R-INLA. He is also interested in Deep/Machine Learning algorithms.

• Topological ranking.
• Data analysis.
• Brain signals .
• Generative Adversarial Networks .
• Topological ranking.