I am an Assistant Professor of Statistics for Public Health at the
King Abdullah University of Science and Technology (KAUST), and the Principal Investigator of the Geospatial Statistics and Health Surveillance Research Group.
I received my Ph.D. in Mathematics from the University of Valencia and my Master's in Biostatistics from Harvard University.
My research focuses on the development of innovative statistical methods and computational tools for geospatial data analysis and health surveillance, and the impact of my work has directly informed strategic policy in reducing disease burden in several countries. 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 (2019, Chapman & Hall/CRC).
I graduated in Mathematics from the University of Valencia with an Erasmus year abroad at the Johannes Gutenberg University of Mainz. Following graduation, I worked in a technological company developing algorithms for optimal investment strategies. After that, I enrolled in the Ph.D. program at the University of Valencia and worked at the office for regional statistics and the national cancer registry. During my doctoral studies, I was awarded the prestigious "la Caixa" Fellowship for studying my Master's degree in Biostatistics at Harvard University, and this complemented my mathematical background with a solid knowledge in biostatistics and epidemiology. I also received an Ibercaja Research Fellowship to carry out a research project at Harvard Medical School, a stipend from Google Summer of Code to write code for the R project, and completed a traineeship at the European Center for Disease Prevention and Control (ECDC). After obtaining my Ph.D. with Extraordinary Award, I was appointed to academic statistics positions at the University of Bath, Lancaster University, Queensland University of Technology, London School of Hygiene & Tropical Medicine, and Harvard School of Public Health.
In 2020 I joined the King Abdullah University of Science and Technology (KAUST) as an Assistant Professor of Statistics for Public Health, and as the Principal Investigator of the Geospatial Statistics and Health Surveillance Research Group. My 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 work 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 SpatialEpiApp, 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 universities in the United Kingdom, Australia, and Ethiopia, and I have been invited to deliver training courses on geospatial modeling, disease mapping, and the development of interactive visualization applications at international conferences and workshops. My publications cover topics in statistical methodology, software, and health and environmental applications, and I am the author of the book Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (2019, Chapman & Hall/CRC).
I am a member of the R Epidemics Consortium (RECON), a group of international experts to create the next generation of analysis tools for disease outbreak response, and a member of the National Aeronautics and Space Administration (NASA) Datanauts, an international community of people interested in learning how to develop data science skills through access to and use of NASA's open data. I am also a member of R-Ladies Global, a worldwide organization to promote gender diversity in the R community.