I lead the Oxford Applied and Theoretical Machine Learning Group (OATML) group. I am an Associate Professor of Machine Learning at the Computer Science department, University of Oxford. I am also the Tutorial Fellow in Computer Science at Christ Church, Oxford, a Turing AI Fellow at the Alan Turing Institute, and Director of Research at the UK Government’s AI Safety Institute (AISI).
Bio: see here
PhD/DPhil applicants for Oxford: Please follow the instructions on the group admissions page.
Postdoc applicants: If you have a strong track record (either coming from machine learning or other fields) and would like to do a postdoc in machine learning, please email me.
Internship applicants: I do not have the capacity to take internship students at the moment. If this changes I will update the status here. Please DO NOT email me asking about this, as your email will not be answered.
Teaching: I tought the following courses:
Advanced Machine Learning: 2018-2019 (link)
Advanced Topics in Machine Learning: 2021-2022 (link, last 4 lectures)
Uncertainty in Deep Learning: 2023-2024 (link, course website: yr.gl/udl23)

Fields I have published work in include Bayesian deep learning • deep learning • approximate Bayesian inference • Gaussian processes • Bayesian modelling • Bayesian non-parametrics • scalable MCMC • generative modelling.
With applications including AI safety • ML interpretability • reinforcement learning • active learning • natural language processing • computer vision • medical analysis.

Broadly speaking, my interests lie in the fields of linguistics, applied maths, and computer science. Most of my work is motivated by problems found at the intersections of these fields, with an arching theme of research being understanding empirically developed machine learning techniques.

A list of publications is available here.