KAUST Assistant Professor Paula Moraga has authored a new textbook, "Spatial Statistics for Data Science: Theory and Practice with R," based on her lectures. The book is available for free on her website and in hard copy through the publisher. Dr. Moraga's research focuses on developing statistical methods and computational tools for geospatial data analysis and health surveillance, with applications in reducing disease burden and identifying high-risk populations. Why it matters: The publication strengthens KAUST's research profile in spatial data science and offers valuable open-source resources for addressing critical challenges in public health and resource management within Saudi Arabia and the broader region.
Dr. Paula Moraga, an Assistant Professor at KAUST, has been awarded the 2023 Letten Prize for her work on disease surveillance systems. The prize recognizes researchers under 45 for contributions to health, development, environment, and equality. Moraga's research enables early epidemic detection, and she was selected from 164 applicants. Why it matters: This award highlights KAUST's contributions to public health research and underscores the importance of AI and data science in addressing global health challenges.
A KAUST research team is using cellphone mobility data, Google searches, and social media to model and predict COVID-19 spread. The models aim to forecast cases in the coming weeks and inform resource allocation, including hospital beds and medical staff. The team is using aggregated and anonymized data from cellphone companies to respect people's privacy. Why it matters: Integrating real-time digital data with epidemiological modeling can improve the speed and effectiveness of public health responses in the region and globally.
MBZUAI Professor Fakhri Karray and co-authors from the University of Waterloo have published "Elements of Dimensionality Reduction and Manifold Learning," a textbook on methods for extracting useful components from large datasets. The book addresses the challenge of the "curse of dimensionality," where growth in datasets complicates their use in machine learning. Karray developed the material from a popular course he taught at Waterloo. Why it matters: The textbook provides a unified resource for students and researchers in machine learning and AI, addressing a foundational challenge in processing high-dimensional data, relevant to diverse applications in the region.
KAUST Ph.D. student Sabrina Vettori won the 2017 Student Paper Competition sponsored by the Section on Statistics and the Environment of the American Statistical Association. Her winning paper was titled "Bayesian clustering and dimension reduction in multivariate air pollution extremes", co-authored by Huser and Genton. The competition focused on environmental statistics, with winners presenting at the Joint Statistical Meetings. Why it matters: This award recognizes KAUST's contribution to environmental statistics and highlights the university's ability to attract and nurture talent in this critical area.