Louis Sharrock
Lecturer in Statistical Science

Dept. of Statistical Science
University College London
1-19 Torrington Place
London, WC1E 7HB
About. I am a Lecturer (Assistant Professor) in the Department of Statistical Science at University College London. I was previously a Senior Research Associate with Prof. Chris Nemeth at Lancaster University, and 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 also 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 lie at the intersection of computational statistics, machine learning, and optimisation, with a particular focus on the design and analysis of scalable algorithms for statistical inference in complex statistical models. My current research focuses on the development of learning-rate-free sampling algorithms, the application of score-based diffusion models to likelihood-free inference, and the design of efficient methods for online parameter estimation in interacting particle systems and mean-field equations.
Supervision. I welcome inquiries from motivated students interested in pursuing a PhD. If you are interested in working with me, I encourage you to take a closer look at my publication list and get in touch if you see a good fit. Further information for prospective students is available here.
news
Jun 2, 2025 | I have just started a new position as Lecturer (Assistant Professor) in the Department of Statistical Science at University College London. |
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Nov 24, 2024 | In Jun 2025, I will give an invited talk at the Isaac Newton Institute workshop on Accelerating statistical inference and experimental design with machine learning. |
Oct 7, 2024 | Our paper - “Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows” - has been accepted to NeurIPS 2024! |
May 23, 2024 | We have a new preprint on “Markovian Flow Matching” on the arXiv! Check it out here. |
May 1, 2024 | Our paper - “Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds” - has also been accepted as a spotlight at ICML 2024! |
May 1, 2024 | Our paper - “Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models” - has been accepted as a spotlight at ICML 2024! |
Jan 19, 2024 | Our paper - “Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting” - has been accepted to AISTATS 2024! |
Oct 20, 2023 | We are organising an exciting workshop at the RSS on Gradient Flows for Sampling, Inference, and Learning. Click here for more details. |
Sep 21, 2023 | Our paper - “Learning Rate Free Sampling 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. |