|Position||Lecturer in Statistical Science|
|Themes||Computational Statistics, General Theory and Methodology, Stochastic Modelling of Complex Systems|
François-Xavier is a Lecturer in Statistical Science at UCL and a Group Leader at The Alan Turing Institute where he leads a programme of research on the Fundamentals of Statistical Machine Learning.
Prior to joining UCL, François-Xavier did his PhD through a joint centre for doctoral training between Warwick and Oxford. He also spent time as a research assistant in the Department of Mathematics at Imperial College London, then as a research associate in the Department of Engineering at the University of Cambridge.
Bayesian inference, computational statistics, intractable models, kernel methods, Monte Carlo methods, Stein's method, uncertainty quantification.
- 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.
- Oates, C. J., Cockayne, J., Briol, F.-X., & Girolami, M. (2019). Convergence rates for a class of estimators based on Stein’s identity. Bernoulli, 25(2), 1141–1159.
- 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).
- Xi, X., Briol, F.-X., & Girolami, M. (2018). Bayesian quadrature for multiple related integrals. In International Conference on Machine Learning, PMLR 80 (pp. 5369–5378).
- Chen, W. Y., Mackey, L., Gorham, J., Briol, F.-X., & Oates, C. J. (2018). Stein Points. In Proceedings of the International Conference on Machine Learning, PMLR 80 (pp. 843–852).