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.
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Flaxman SR, Teh YW, Sejdinovic D, Poisson Intensity Estimation with Reproducing Kernels, Electronic Journal of Statistics, ISSN:1935-7524
et al., 2017, Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization, Journal of the Royal Society Interface, Vol:14, ISSN:1742-5689
et al., 2017, Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis, Lancet Global Health, Vol:5, ISSN:2214-109X, Pages:E888-E897
et al., 2017, Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis, Lancet Global Health, Vol:5, ISSN:2214-109X, Pages:E1221-E1234
Flaxman SR, Neill DB, Smola AJ, 2016, Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference, Acm Transactions on Intelligent Systems and Technology, Vol:7, ISSN:2157-6904