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| Position | Associate Professor in Statistical Science |
| Phone (external) | |
| Phone (internal) | |
| Email(*) | f.briol |
| Personal webpage | https://fxbriol.github.io/ |
| Themes | Computational Statistics, General Theory and Methodology, Environmental Statistics |
* @ucl.ac.uk
Biographical Details
Dr François-Xavier Briol is an Associate Professor in Statistical Science at UCL. He is also a Group Leader in Data-Centric Engineering at The Alan Turing Institute, the UK's national institute for Data Science and AI, where he leads a programme of research on the Fundamentals of Statistical Machine Learning.
Research Interests
Dr François-Xavier Briol's research interests span the intersection of computational statistics, machine learning and applied mathematics. His work primarily focuses on methodology for statistical computation and inference for large scale and computationally expensive probabilistic models.
Selected publications
- Dellaporta, C., Knoblauch, J., Damoulas, T., & Briol, F.-X. (2022). Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap. International Conference on Artificial Intelligence and Statistics (AISTATS), 943–970. Received Best Paper Award.
- Matsubara, T., Knoblauch, J., Briol, F.-X., & Oates, C. J. (2022). Robust generalised Bayesian inference for intractable likelihoods. Journal of the Royal Statistical Society B: Statistical Methodology.
- Wynne, G., Briol, F.-X., & Girolami, M. (2021). Convergence guarantees for Gaussian process means with misspecified likelihoods and smoothness. Journal of Machine Learning Research, 22(123), 1–40.
- Briol, F.-X., Oates, C. J., Girolami, M., Osborne, M. A., & Sejdinovic, D. (2019). Probabilistic integration: A role in statistical computation? (with discussion). Statistical Science, 34(1), 1–22.
- Barp, A., Briol, F.-X., Duncan, A. B., Girolami, M., & Mackey, L. (2019). Minimum Stein discrepancy estimators. In Neural Information Processing Systems (pp 12964-12976).