About us
The Fundamentals of Statistical Machine Learning group is a research group in the UCL Department of Statistical Science. Our focus is on the intersection of statistical inference and machine learning methodology and theory.
Meet the Team
Faculty
Alessandro Barp
Assistant Professor
François-Xavier Briol
Professor of Statistics and Machine Learning
Jeremias Knoblauch
Associate Professor
Louis Sharrock
Assistant Professor
Postdoctoral Researchers
Harita Dellaporta
Postdoctoral Researcher
Masha Naslidnyk
Postdoctoral Researcher
PhD Students
Abdelhamid Ezzerg
PhD Student
Arina Odnoblyudova
PhD Student
Guilherme Pedrilho Soares
PhD Student
Lewis Cornwall
PhD Student
Marta Piperno
PhD Student
Stefanos Ioannou
PhD Student
William Laplante
PhD Student
Yann McLatchie
PhD Student
Ahab Isaac
PhD Student
Zixiao Hu
PhD Student
Visiting Researchers
Sophia Kang
Visiting Researcher
Yuga Hikida
Visiting Researcher
Yuta Ono
Visiting Researcher
Alumni
Ayush Bharti
Visitor, Autumn 2022
Ieva Kazlauskaitė
Postdoc, 2023-2024
Ilina Yozova
PhD, 2021-2026
Joshua Rooijakkers
Visitor 2025
Kaiyu Li
PhD, 2019-2024
Matias Altamirano
PhD, 2022-2026
Oscar Key
PhD, 2020-2025
Takuo Matsubara
Visitor, 2019-2023
Veit Wild
Visitor, Autumn 2023
Xing Liu
Visitor, 2023-2024
Zhuo Sun
PhD, 2019-2023
Zonghao Chen
PhD, 2022-2026
News
F-X is visiting the department of Statistics at the University of Auckland this term.
William is taking up a visiting fellowship with Prof Chris Oates at Newcastle University this term.
Welcome to Marta and Stefanos who are joining the group as PhD students via the CCMI CDT. Marta joins us from the Smith Institute, where she was a mathematical consultant, and Stefanos joins us from the University of Cambridge, where he was a research assistant in AI and Neuroscience.
Welcome to Steve, who is joining the group as a research assistant working with F-X. Steve was previously a PhD student with Ricardo Silva and Arthur Gretton at UCL.
Yuta Ono joins the group as a visiting researcher. Yuta will be doing his PhD with Yingzhen Li at Imperial College London, and will be co-supervised by F-X.
Recent Publications
(2026).
A computationally-tractable measure of global sensitivity for sampling-based Bayesian inference.
(2026).
Conservative neural posterior estimation via distributionally robust training.
In NeurIPS 2026.
(2026).
Multi-Output Robust and Conjugate Gaussian Processes.
In NeurIPS 2026.
(2026).
Concept frustration: Aligning human concepts and machine representations.
In NeurIPS.
(2026).
Robust Bayesian Optimisation with Unbounded Corruptions.
In ICML 2026.
(2026).
Thinned Mean Field Langevin Dynamics.
In ICML 2026.
(2026).
TabMGP: Martingale posterior with TabPFN.
In ICML 2026.
Major Research Funding
UKRI/Turing Project “Digital Twin Handbook” (co-I: Briol)
UCL EPSRC-funded Post-doctoral Extension Award. Project on “Reliable Insights from Scientific Simulators” (PI: Dellaporta)
Turing Project: “Bayesian Robustness in Filtering Algorithms” (PI: Briol)
Bloomberg Data Science Ph.D. Fellowship Program (PI: Altamirano)
EPSRC New Investigator Award. Project on ‘Transfer Learning for Monte Carlo Methods’ (PI: Briol)
EPSRC Small Grant in the Mathematical Sciences, Project on “Robust Foundations for Bayesian Inference” (PI: Briol, co-I: Knoblauch)
EP/W005859/1: EPSRC Fellowship, Project on “Optimisation-centric Generalisations of Bayesian Inference” (PI: Knoblauch)
Biometrika fellowship: “Generalising Bayesian Inference” (PI: Knoblauch)
Amazon Research Award: Project on “Transfer Learning for Numerical Integration in Expensive Machine Learning Systems” (PI: Briol)
Contact
- 1-19 torrington place, London, WC1E 7HB