Photograph of Seth

COVID-19 research: I am working with colleagues from Imperial’s Department of Mathematics and School of Public Health to model the spread of COVID-19.

Peer reviewed: “Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe”, Flaxman, Mishra, Gandy et al, Nature, 2020 with accompanying website: https://mrc-ide.github.io/covid19estimates/

Reports (under review):

– USA: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-23-united-states/ and website: https://mrc-ide.github.io/covid19usa/
– Brazil: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-21-brazil/
– Italy: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-20-italy/

Up to date news coverage of my research can be found here.

I am a senior lecturer in statistical machine learning in the statistics section of the Department of Mathematics at Imperial College London. I help lead the Machine Learning Initiative at Imperial and the EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning (StatML) at Imperial and Oxford. 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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