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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et al., Predictor Variable Prioritization in Nonlinear Models: A Genetic Association Case Study
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., AdaGeo: Adaptive Geometric Learning for Optimization and Sampling, AISTATS