I am currently a third year PhD student supervised by Professor Mark Girolami and am part of a joint PhD program between the statistics departments at the University of Warwick and the University of Oxford (OxWaSP). Since September 2016, I am also a visiting student in the Department of Mathematics at Imperial College London.
My main academic interests are at the interface of computational statistics, machine learning and applied mathematics. My work focuses on probabilistic numerical methods. I am part of the 2017-2018 SAMSI working group on probabilistic numerics and organise regular virtual talks on the topic. You can also see the following page for a summary of some of my research.
More recently, I have also been working on Monte Carlo methods, with a special focus on applications of Stein's method and geometry to constructing control variates and Markov Chain Monte Carlo samplers.
NEWS: Our paper on "Probabilistic Models for Integration Error in the Assesment of Functional Cardiac Models" has just been accepted to NIPS! And we also have a nice intro video to it.
- Oates, C. J., Niederer, S., Lee, A., Briol, F-X. & Girolami, M. (2017). Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models. arXiv:1606.06841. Accepted for publication at "Advances in Neural Information Processing Systems" (NIPS), [arXiv][video]
- Briol, F-X., Oates, C. J., Cockayne, J., Chen, W. Y. & Girolami, M. (2017). On the Sampling Problem for Kernel Quadrature. Proceedings of the 34th International Conference on Machine Learning, PMLR 70:586-595, 2017. [arXiv] [conference]
- Barp, A., Briol, F-X., Kennedy, A. D. & Girolami, M. (2017). Geometry and Dynamics for Markov Chain Monte Carlo. arXiv:1705.02891. Accepted for publication in "Annual Review of Statistics and Its Applications".[arXiv] [journal]
- Oates, C. J., Cockayne, J., Briol, F-X. & Girolami, M. (2016). Convergence Rates for a Class of Estimators Based on Stein's Identity. arXiv:1603.03220. [arXiv]
- Briol, F-X., Oates, C. J., Girolami, M., Osborne, M. A. & Sejdinovic, D. (2015). Probabilistic Integration: A Role for Statisticians in Numerical Analysis? [Student Paper award 2016 from the Section on Bayesian Statistical Science of the ASA] [arXiv] [code] [blog post by A. Gelman] [blog post by C. Robert]
- Briol, F-X., Oates, C. J., Girolami, M. & Osborne, M. A. (2015). Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. Advances In Neural Information Processing Systems (NIPS), pages 1162-1170. [accepted with spotlight presentation (top 4.5% of submitted papers)]. [arXiv] [conference] [blog post by I. Schuster]
- Barp, A., Barp, E. G., Briol, F-X. & Ueltschi, D. (2015). A numerical study of the 3D random interchange and random loop models. Journal of Physics A: Mathematical and Theoretical, 48(34). [journal] [arXiv]
- Briol, F-X. & Girolami, M. (2017). Bayesian Numerical Methods as a Case Study for Statistical Data Science. to appear at "Statistical Data Science". [pdf]
- Briol, F-X., Cockayne, J. & Teymur, O. (2016). Contributed Discussion on Article by Chkrebtii, Campbell, Calderhead, and Girolami. Bayesian Analysis, Vol 11, Num 4, pp1285-1293. [journal] [arXiv]
For more details, see also my Google scholar profile.
See the following page for a list of previous talks and posters presented.
f-x.briol at warwick.ac.uk
- f.briol16 at ic.ac.uk