Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Lecturer in Machine Learning and Big Data







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.



Crawford L, Flaxman SR, Runcie DE, 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

Bhatt S, Cameron E, Flaxman SR, 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


Abbati G, Tosi A, Osborne M, et al., AdaGeo: Adaptive Geometric Learning for Optimization and Sampling, AISTATS

Law HCL, Sutherland D, Sejdinovic D, et al., Bayesian Approaches to Distribution Regression, AISTATS 2018

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