Statistical Science


Dr François-Xavier Briol

PositionLecturer in Statistical Science
Phone (external) 
Phone (internal) 
Personal webpagehttps://fxbriol.github.io/
ThemesComputational Statistics, General Theory and MethodologyStochastic Modelling of Complex Systems

* @ucl.ac.uk

Biographical Details

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.

Research Interests

Bayesian inference, computational statistics, intractable models, kernel methods, Monte Carlo methods, Stein's method, uncertainty quantification.

Selected publications

  • 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).