Statistical Science


Dr François-Xavier Briol

PositionLecturer in Statistical Science
Phone (external)+44(0)20 7679 8294
Phone (internal)48294
Personal webpagehttps://fxbriol.github.io/
ThemesComputational Statistics, General Theory and MethodologyStochastic Modelling and Time Series

* @ucl.ac.uk

Biographical Details

François-Xavier is a Lecturer in Statistical Science at UCL since August 2019, and is also a visiting researcher at The Alan Turing Institute where he is affiliated to the data-centric engineering programme.

Prior to UCL, François-Xavier did his PhD on a joint centre for doctoral training between Warwick and Oxford, graduating in April 2019. He also spent time as a research assistant in the Department of Mathematics at Imperial College London (July 2018 - March 2019), then as a research associate in the Department of Engineering at the University of Cambridge (April 2019 - July 2019).

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.
  • Briol, F.-X., Barp, A., Duncan, A. B., & Girolami, M. (2019). Statistical inference for generative models with maximum mean discrepancy. arXiv:1906.05944.
  • Barp, A., Briol, F.-X., Duncan, A. B., Girolami, M., & Mackey, L. (2019). Minimum Stein discrepancy estimators. arXiv:1906.08283.
  • 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).