Statistical and machine learning methods for health and environmental applications.
Project Details
Program
Statistics
Field of Study
statistics, mathematics, computer science
Division
Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link
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
About the Researcher
Paula Moraga
Assistant Professor, Statistics
Affiliations
Education Profile
- Ph.D. Mathematics, University of Valencia, 2012
- M.Sc. Biostatistics, Harvard University, 2011
- B.Sc. Mathematics, University of Valencia, 2006
Research Interests
a€‹Professor Moraga's research focuses on the development of innovative statistical methods and computational tools for geospatial data analysis and health surveillance including methods to understand the 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. Professor Moraga's work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries.Selected Publications
- P. Moraga. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Chapman & Hall/CRC Press, ISBN 978-0367357955, 2019.
- P. Moraga, I. Dorigatti, Zhian N. Kamvar, P. Piatkowski, Salla E. Toikkanen, VP Nagraj, C. A. Donnelly, and T. Jombart. epiflows: an R package for risk assessment of travel-related spread of disease. F1000Research, 7:1374, 2019.
- P. Moraga. SpatialEpiApp: A Shiny web application for the analysis of spatial and spatiotemporal disease data. Spatial and Spatio-temporal Epidemiology, 23:47a-57, 2017.
- P. Moraga, S. Cramb, K. Mengersen, and M. Pagano. A geostatistical model for combined analysis of point-level and area-level data using INLA and SPDE. Spatial Statistics, 21:27a-41, 2017.
- P. Moraga, J. Cano, R. F. Baggaley, J. O. Gyapong, S. Njenga, B. Nikolay, E. Davies, M. P. Rebollo, R. L. Pullan, M. J. Bockarie, D. Hollingsworth, M. Gambhir, and S. J. Brooker. Modelling the distribution and transmission intensity of lymphatic filariasis in sub-Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling. Parasites & Vectors, 8:560, 2015.
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