My work involves modelling infectious disease transmission dynamics.
My research involves using viral sequence data and patient data to characterise HIV transmission and monitor the effectiveness of interventions at city-level.
My research is on scalable methods and flexible models for spatiotemporal statistics and Bayesian machine learning, applied to public policy and social science.
I develop statistical machine learning methods to solve scientific questions with impact in real life.
I primarily work at intersection of public health, machine learning and Bayeasian modelling.
I I work at the intersection of Bayesian inference, spatial statistics and epidemiology.
I focus on mathematical, statistical and computer science tools to answer questions about human health.
I develop bespoke statistical methods for public good. My group and I are particularly interested in novel Bayesian methods that harness information in viral deep sequence data, mobile phone mobility data, and time-resolved patient data to characterise the spread of infectious diseases, and to guide public health interventions.