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 the statistics section of the Department of Mathematics at Imperial College London. I help lead the Machine Learning Initiative at Imperial and the StatML CDT (Imperial/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. Find more information on my website.
et al., 2020, Prevalence and causes of vision loss in East Asia in 2015: magnitude, temporal trends and projections, British Journal of Ophthalmology, Vol:104, ISSN:0007-1161, Pages:616-622
et al., 2019, Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish., Journal of Biophotonics, Vol:12, ISSN:1864-063X
et al., 2019, Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances, Vol:5, ISSN:2375-2548, Pages:1-15