Bristol, BS8 1UG
About. I am a Senior Research Associate in Statistical Machine Learning working with Prof. Chris Nemeth at Lancaster University, and an Honorary Senior Research Associate at the University of Bristol. I was previously a Data Science Heilbronn Research Fellow at the University of Bristol. I obtained my PhD in the Department of Mathematics at Imperial College London, supervised by Dr. Nikolas Kantas. I hold an MRes in Mathematics and an MSc in Statistics from Imperial College London, and an MA in Mathematics from the University of Cambridge.
Research. My research interests include computational statistics, machine learning, and optimisation, with a particular focus on stochastic gradient Markov Chain Monte Carlo methods and likelihood free inference. My current research focuses on learning-rate free sampling algorithms, score-based methods for simulation based inference, and online inference for interacting particle systems and mean-field equations.
|Sep 21, 2023||Our paper - “Learning Rate Free Bayesian Inference in Constrained Domains” - has been accepted to NeurIPS 2023!|
|Sep 8, 2023||In Feb 2024, I will give an invited talk on parameter-free optimisation on the space of probability measures at ISMP 2024.|
|Aug 14, 2023||In Feb 2024, I will give an invited talk on online parameter estimation for interacting particle systems at the SIAM Conference on Uncertainty Quantification.|
|May 24, 2023||We have two new preprints up on arXiv! Check them out here and here.|
|May 3, 2023||Our paper - “Online Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation” - has been accepted to Stochastic Processes and their Applications.|
|Apr 24, 2023||Our paper - “Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates” - has been accepted to ICML 2023!|
|Apr 17, 2023||On 9th June I will give a talk about our recent work on coin sampling in the OxCSML seminar series.|
Online Parameter Estimation for the McKean-Vlasov Stochastic Differential EquationStochastic Processes and their Applications, 2023
Two-timescale stochastic gradient descent in continuous time with applications to joint online parameter estimation and optimal sensor placementBernoulli, 2023
Coin Sampling: Gradient-Based Bayesian Inference without Learning RatesProceedings of the 40th International Conference on Machine Learning (ICML 2023), 2023
Learning Rate Free Bayesian Inference in Constrained DomainsTo appear in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2023