Michael Whitehouse
Statistics
School of Public Health, Imperial College
michael.whitehouse@imperial.ac.ukSummary of research
I am currently a post-doc at Imperial College working on statistical and probabilistic methodology in epidemiology with Dr Kris Parag, in particular inference and control for agent/individual-based models of epidemics.
Previously, I was a student at the Centre for Doctoral Training in Computational Statistics and Data Science at the University of Bristol, under the supervision of Professor Nick Whiteley. My PhD rsearch focused on principled approximate inference in stochastic compartmental models of epidemics.
After my PhD I spent a short period as a Postdoctoral Research Associate, working with Prof. Christophe Andrieu on the Bayes4Health grant, investigating the theoretical underpinnings of generative diffusion models. During this time I lectured part of a third year Statistical Machine Learning Course.
Past research projects include time series analysis and agent based modelling of asset returns. My undergraduate final year project concerned the modelling of webgraphs.
Publications
Scalable calibration for partially observed individual-based epidemic models through categorical approximations Lorenzo Rimella, Nick Whiteley, Chris Jewell, Paul Fearnhead, Michael Whitehouse. Arxiv Preprint, 2025.
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods Michael Whitehouse, Nick Whiteley, Lorenzo Rimella. Journal of the Royal Statistical Society: Series B (statistical methodology), 2023.
Contribution to the Discussion of “the Discussion Meeting on Probabilistic and statistical aspects of machine learning” Pierre-Aurelien Gilliot, Christophe Andrieu, Anthony Lee, Song Liu, and Michael Whitehouse. Journal of the Royal Statistical Society: Series B (statistical methodology), 2024.
Trend, Value, and crashes in financial markets: an agent based model for asset prices Working paper.
Talks
COMPSTAT 2022 - Bologna 23-26 August 2022
Compass Conference 2022 - Bristol 13 September 2022
Lancaster University Computational Statistics and Machine Learning group 2022 - Lancaster 6 October 2022
Bayescomp 2023 - Levi, Finland 12-17 March 2023
Machine Learning & Global Health seminar - Online 29 Jan 2025
Juniper seminar - Online 5 Feb 2025
Teaching
Lecturer:
Modern Statistics and Machine Learning for Population Health African Institute for Mathematical Science, Capetown South Africa, 2025
Statistical Machine Learning 3rd year (2 weeks on regression)University of Bristol, 2024
Tutorial leader, University of Bristol:
Statistics and probability 1 2020
Time series analysis 3 2020-2022
Further topics in probability 3/4 2022
In the media
I contributed some highly caveated analysis to a Sky News report on the potential impact of the pandemic on the Tokyo Olympics. This came in the form of some pretty naive modelling of the rise of COVID-19 cases in Japan in the summer of 2021 - not too epidemiologically interesting but a fun exercise nonetheless!
