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
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
Matias Altamirano
PhD Student
William Laplante
PhD Student
Yann McLatchie
PhD Student
Ahab Isaac
PhD Student
Zixiao Hu
PhD Student
Zonghao (Hudson) Chen
PhD Student
Visiting Researchers
Yuga Hikida
Visiting Researcher
Alumni
Ayush Bharti
Visitor, Autumn 2022
Ieva Kazlauskaitė
IMSS Fellow, 2023-2024
Ilina Yozova
PhD Student
Joshua Rooijakkers
Visiting Researcher
Kaiyu Li
PhD, 2019-2024
Oscar Key
PhD, 2020-2025
Veit Wild
Visitor, Autumn 2023
Xing Liu
Visitor, 2023-2024
Zhuo Sun
PhD, 2019-2023
News
William will be taking up a three months internship at Xantium, where he will be working on machine learning for quantitative finance.
Our group is organising the UCL Institute of Mathematical and Statistical Science Annual Lecture on the 27th April 2026. The theme this year is “Computational Statistics and Machine Learning”, and the keynote speaker is Dr Lester Mackey.
Our group is organising the London Meeting on Computational Statistics on the 28th and 29th April 2026. We welcome contributed talks and posters!
Hudson will be visiting Wharton Business School’s department of Statistics and Data Science, where he will be spending this month working with Weijie Su.
William will be visiting the approximate inference lab at Aalto University for the next two months, where he will be working with Ayush Bharti.
Recent Publications
(2025).
Multilevel neural simulation-based inference.
In NeurIPS 2025.
(2025).
Stationary MMD Points.
(2025).
Kernel Quantile Embeddings and Associated Probability Metrics.
In ICML.
(2025).
Prediction-Centric Uncertainty Quantification via MMD.
In AISTATS.
(2025).
Nested Expectations with kernel Quadrature.
In ICML.
Major Research Funding
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)
UKRI/Turing Project “Digital Twin Handbook” (co-I: Briol)
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