About Me

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)


You can find more up-to-date news here

New research group!


We've started a general ML research group in Oxford CS: Oxford Applied and Theoretical Machine Learning Group (OATML).

New NIPS papers and Third Bayesian Deep Learning workshop


Publications page has been updated with new NIPS papers. We're also organising the Third Bayesian Deep Learning workshop at NIPS 2018.

Updated papers online


Publications page has been updated with 12 new papers [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ]. Lots of new work with collaborators and students!

Teaching Machine Learning at NASA


I will again teach machine learning at the NASA Frontier Development Lab this summer, helping NASA make use of AI for the space program.

Research interests

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.

Curriculum vitae

My Résumé is available here.

Contact me




Computer Science Department
University of Oxford
Oxford, OX1 3QD
United Kingdom