# Louis Sharrock

Senior Research Associate in Statistical Machine Learning

Fry Building

Woodland Road

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.

## news

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. |