My research can be characterized as "spatial data science" and it arises out of the application of quantitative methods to geographical data. Specifically, it involves the application of space-time statistics and geostatistics, machine learning and spatially explicit numerical modeling, to Earth observation (EO) and other environmental and social data, to answer a wide range of science and social science questions. My research is, thus, highly inter-disciplinary and spans a wide range of statistical and numerical techniques and a wide range of science and social science domains.
I have a major interest in the spatial epidemiology of vector-borne disease transmission systems, such as malaria and Trypanosomiasis, based on:
- Spatial and spatio-temporal regression models to predict risk (e.g., incidence, prevalence) in space-time so that interventions can be targeted.
- Agent-based models of the transmission dynamics such as to connect management changes in the landscape with disease and poverty outcomes.
I am keen to supervise exceptional PhD students with a strong mathematical, statistical or computer science background, who have an interest in these research topics. If you are interested in applying for a PhD in any of these topics please contact me at email@example.com.