Statistical and machine learning methods for health and environmental applications.Apply
Assistant Professor, Statistics and Principal Investigator, Geospatial Statistics and Health Surveillance
Paula Moraga received her Ph.D. in Mathematics from the University of Valencia, and her Master's in Biostatistics from Harvard University. Prior to KAUST, she was appointed to academic statistics positions at Lancaster University, Harvard School of Public Health, London School of Hygiene & Tropical Medicine, Queensland University of Technology and University of Bath.
Paula's research focuses on the development of innovative statistical methods and computational tools for geospatial data analysis and health surveillance including methods to understand geographic and temporal patterns of diseases, assess their relationship with potential risk factors, detect clusters, and evaluate the impact of interventions. She is also interested in the development of statistical software including R packages and interactive visualization applications for reproducible research and communication.
Paula has published extensively in leading journals and is the author of the book 'Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny' (2019, Chapman & Hall/CRC) (https://www.paulamoraga.com/book-geospatial/).