Leverhulme Trust Research Fellowship
October 2025
After several years balancing research, teaching and administration responsibilities — including a recent spell as Head of the Statistics and Probability Section in the School of Mathematical Sciences at the University of Nottingham — I’m incredibly excited to have the chance to focus deeply on research again.
A Research Fellowship will give me the time and space to pursue new ideas in the topical area of sample-based-inference (SBI) or stochastic processes, collaborate with people, travel and, most importantly, learn new things!
I’m very grateful to The Leverhulme Trust for this opportunity — and looking forward to what comes next!
NIHR Grant Awarded
October 2025
Phil O'Neill and I have been recently awarded a grant from NIHR (National Institute for Health and Care Research) to advance Bayesian inference for stochastic epidemic models by developing the next generation of statistical methods for fitting epidemic models to infectious disease data which go beyond traditional data-augmentation approaches. In particular, the project is concerned with developing analytic likelihood approximation methods and AI-power methods for robust, efficient and scalable inference for both final outcome and temporal data.
Recent paper published in Bayesian Analysis
July 2025
Epidemics are inherently stochastic, and stochastic models provide an appropriate way to describe and analyse such phenomena. Given temporal incidence data consisting of, for example, the number of new infections or removals in a given time window, a continuous-time discrete-valued Markov process provides a natural description of the dynamics of each model component, typically taken to be the number of susceptible, exposed, infected or removed individuals.
Fitting the SEIR model to time-course data is a challenging problem due incomplete observations and, consequently, the intractability of the observed-data likelihood. Whilst sampling based inference schemes such as Markov chain Monte Carlo are routinely applied, their computational cost typically restricts analysis to data sets of no more than a few thousand infective cases. Instead, we have developed a sequential inference scheme that makes use of a computationally cheap approximation of the most natural Markov process model.
This work has been accepted for publication in a Bayesian Analysis is part of Sam Whitaker supervised by Andrew Goligthly (Durham) and Colin Gillespie (Newcastle) and be accesed from this link.