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

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Alessandro Barp

Assistant Professor

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François-Xavier Briol

Associate Professor

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Jeremias Knoblauch

Associate Professor

Postdoctoral Researchers

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Harita Dellaporta

Postdoctoral Researcher

PhD Students

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Ilina Yozova

PhD Student

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Masha Naslidnyk

PhD Student

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William Laplante

PhD Student

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Yann McLatchie

PhD Student

Visiting Researchers

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Saifuddin Syed

Florence Nightingale Bicentennial Fellow

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Yuga Hikida

Visiting Researcher

Alumni

Ayush Bharti

Visitor, Autumn 2022

Ieva Kazlauskaitė

IMSS Fellow, 2023-2024

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

Tune-in to the Post-Bayesian Seminar Series!

This seminar series explores “post-Bayesian” methods, which extend beyond traditional Bayesian inference to address its limitations in modern machine learning settings.

The seminar will run fortnightly from mid-February onwards. The first iteration of the series will be broken down into three ‘chapters’ consisting of between 4-6 talks in each chapter. Each chapter will focus on a different set of post-Bayesian ideas: generalised Bayes (led by Jeremias Knoblauch), predictive resampling-based ideas like Martingale posteriors (led by Edwin Fong), and PAC-Bayes (led by Pierre Alquier). To make this useful for the entire community, the talks in each chapter will seek to cover some key aspects of literature conducted under that chapter.

For more information, please visit the seminar series’ web page or register in the seminar’s mailing list.

News

F-X and Jeremias are organising a satellite workshop at the BayesComp conference on the topic of ‘Bayesian Computation and Inference with Misspecified Models’. The workshop website is available here.
We’re thrilled that the Advances in Post-Bayesian Methods workshop was such a success, with over 120 attendees and fantastic talks!
You can now watch all the talks here.
Oscar completed his Phd and will join PriorLabs to work on foundational models for tabular data.

Recent Publications

Major Research Funding

Turing Project: “Bayesian Robustness in Filtering Algorithms” (PI: Briol)
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