PhD
Diffusion Generative Modelling for Divide-and-Conquer MCMC (preprint). Divide-and-conquer MCMC is a strategy for parallelising Markov Chain Monte Carlo sampling by running independent samplers on disjoint subsets of a dataset and merging their output. An ongoing challenge in the literature is to efficiently perform this merging without imposing distributional assumptions on the posteriors. We propose using diffusion generative modelling to fit density approximations to the subposterior distributions. This approach outperforms existing methods on challenging merging problems, while its computational cost scales more efficiently to high dimensional problems than existing density estimation approaches.
MRes
Research proposal: Diffusion-Based Deep Generative Models for Assessing Safety in Autonomous Vehicles. An introduction to deep generative models in the context of generating scenarios to test autonomous vehicle safety in simulators, with a particular focus on diffusion-based models.
Stochastic Dynamic Optimisation. An introduction to the properties, solution methods, and applications of Markov decision processes and stochastic games.
The Particle Filter. An introduction to particle filtering and particle MCMC, with applications to epidemic modelling.
Undergraduate
Master’s project: Statistics and Data Science for Text Data (2021). An introduction to the field of natural language processing with a particular focus on language modelling. Poster and presentation focus on word embeddings.
STOR-i internship project: Approximate posterior sampling via stochastic optimisation (2019). An overview of how stochastic gradient Markov chain Monte Carlo algorithms can be used for computationally efficient Bayesian inference.