Statistical and machine learning methods for health and environmental applications.

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Project Description

The student will work on the development of statistical and machine learning methods for health and environmental applications. The topic is flexible and potential research areas include disease mapping, early detection of disease outbreaks, air pollution modeling, forest fires prediction, integration of misaligned spatial and spatio-temporal data, and the development of R packages for data analysis and visualization. Examples of research projects can be found at https://www.paulamoraga.com/research
Program - Statistics
Division - Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link - https://www.paulamoraga.com
Field of Study - statistics, mathematics, computer science

About the
Researcher

Paula Moraga

Assistant Professor, Statistics and Principal Investigator, Geospatial Statistics and Health Surveillance

Paula Moraga

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/).

 

Desired Project Deliverables

The student will work on the development of statistical and machine learning methods for health and environmental applications. The topic is flexible and potential research areas include disease mapping, early detection of disease outbreaks, air pollution modeling, forest fires prediction, integration of misaligned spatial and spatio-temporal data, and the development of R packages for data analysis and visualization. Examples of research projects can be found at https://www.paulamoraga.com/research