Position | Lecturer in Statistical Science |

Phone (external) | +44(0)20 7679 8294 |

Phone (internal) | 48294 |

Email(*) | f.briol |

Personal webpage | https://fxbriol.github.io/ |

Themes | Computational Statistics, General Theory and Methodology, Stochastic 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).