I am a lecturer in the statistics section of the Department of Mathematics at Imperial College London, joint with the Data Science Institute. I am also part of the Machine Learning Initiative at Imperial. 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, crime, voting patterns, filter bubbles / echo chambers in media, the regulation of machine learning algorithms, and emotion.
Find more information on my website.
et al., Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge", Annals of Applied Statistics, ISSN:1932-6157
et al., 2019, Mapping changes in housing in sub-Saharan Africa from 2000 to 2015, Nature, ISSN:0028-0836
et al., Prevalence and causes of blindness and vision impairment magnitude, Temporal Trends, and Projections in South and Central Asia, British Journal of Ophthalmology, ISSN:0007-1161
et al., Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study, Annals of Applied Statistics, ISSN:1932-6157
et al., Prevalence and causes of vision loss in South-east Asia and Oceania in 2015: magnitude, temporal trends, and projections, British Journal of Ophthalmology, ISSN:0007-1161