Join my Geospatial Statistics and Health Surveillance research group at KAUST!
I am moving to
I am looking for outstanding PhD students and Postdocs to join my group.
You will work on the development of innovative statistical methods and computational tools for health and environmental applications, and collaborate with scientists around the world on projects that make a positive impact on the health and wellbeing of the population.
Projects will be related to disease mapping, early detection of disease outbreaks, integration of spatial and spatio-temporal data, and development of R packages.
PhD in Statistics
PhD applicants should have a Bachelor's or Master's degree in statistics, mathematics, or a related quantitative field.
General information about the admissions can be found
If you are interested in studying for a PhD in Statistics please send me an e-mail with your CV, motivation letter, and names of three referees, and submit your application through the
KAUST admissions website.
Postdoc applicants should have a PhD in statistics, mathematics, computer science, or a related quantitative field. Experience in spatial statistics, Bayesian modeling or spatial epidemiology would be an advantage.
If you are interested in a postdoc position, please send me an e-mail with your CV including a list of publications, motivation letter, and names of three referees.
KAUST offers an excellent research environment, free tuition, monthly living allowance, housing, medical insurance, and relocation support. More information about the university and the statistics program can be found in the links below, and I am happy to answer any question you may have.
I am a Lecturer in the Department of Mathematical Sciences at the University of Bath, UK, and starting Fall 2020 an Assistant Professor of Statistics for Public Health at the
King Abdullah University of Science and Technology (KAUST).
Before coming to Bath, I held academic positions at Lancaster University, Queensland University of Technology, London School of Hygiene and Tropical Medicine, and Harvard School of Public Health.
My research focuses on the development of innovative statistical methods and computational tools for geospatial data analysis and health surveillance, and has directly informed strategic policy in reducing disease burden in several countries. Past projects include the development
of modeling architectures to understand the spatio-temporal patterns and identify targets for intervention of malaria in Africa, leptospirosis in Brazil, and cancer in Australia.
I have worked on the development of a number of R packages for disease modeling, detection of clusters, and risk assessment of travel-related spread of disease, and I am the author of
a Shiny web application for the analysis of spatial and spatio-temporal disease data.
I have taught statistics and spatial epidemiology courses at both undergraduate and graduate levels at several universities, and I have been invited to deliver training courses on geospatial modeling, disease mapping and R at international conferences and workshops.
I have published extensively in leading journals, and I am the author of the book
Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny.
I received my Bachelor's in Mathematics and my Ph.D. in Statistics from the University of Valencia, and my Master's in Biostatistics from Harvard University.