I am interested in the application of Bayesian modelling and phylogenetics to understand and describe HIV transmission.
My work involves modelling infectious disease transmission dynamics.
I use machine learning methods to understand infectious diseases 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 Bayesian 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.