I am an associate professor at the University of Oxford in the Department of Computer Science and a tutorial fellow of Jesus College. My research is on scalable methods and flexible models for spatiotemporal statistics and Bayesian machine learning, applied to public policy and social science. I’ve worked on application areas that include public health, orphanhood, deep learning from satellite and street-level imagery, crime, voting patterns, filter bubbles / echo chambers in media, the big data paradox, the regulation of machine learning algorithms, and emotion.

I am working with colleagues from University of Oxford, Imperial College London, and University of Copenhagen to model the spread of COVID-19. Peer reviewed COVID-19 research includes:

“Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe”, Flaxman, Mishra, Gandy et al, Nature, 2020 with accompanying R package.

“Age groups that sustain resurging COVID-19 epidemics in the United States” Monod et al, Science, 2021.

“State-level tracking of COVID-19 in the United States” Unwin et al, Nature Communications, 2020.

“Inference of COVID-19 epidemiological distributions from Brazilian hospital data” Hawryluk et al, 2020.

“Have deaths from COVID-19 in Europe plateaued due to herd immunity?” Okell et al, Lancet, 2020.

Local area UK estimates of cases and the reproduction number R: updated daily.

News coverage of research: click here.