Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Senior Lecturer in Statistical Machine Learning







6M47Huxley BuildingSouth Kensington Campus





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.



Davis SPX, Kumar S, Alexandrov Y, et al., 2019, Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish., Journal of Biophotonics, ISSN:1864-063X

Flaxman S, Chirico M, Pereira P, 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

Keeffe J, Casson R, Pesudovs K, et al., 2019, Prevalence and causes of vision loss in South-east Asia and Oceania in 2015: magnitude, temporal trends, and projections, British Journal of Ophthalmology, Vol:103, ISSN:0007-1161, Pages:878-884

Nangia V, Jonas J, George R, et al., 2019, Prevalence and causes of blindness and vision impairment magnitude, Temporal Trends, and Projections in South and Central Asia, British Journal of Ophthalmology, Vol:103, ISSN:0007-1161, Pages:871-877

Tusting LS, Bisanzio D, Alabaster G, et al., 2019, Mapping changes in housing in sub-Saharan Africa from 2000 to 2015, Nature, ISSN:0028-0836

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