I am an Associate Professor of Machine Learning at the University of Oxford Computer Science department, and head of the Oxford Applied and Theoretical Machine Learning Group (OATML). I am also the Tutorial Fellow in Computer Science at Christ Church, Oxford, and Fellow at the Alan Turing Institute, the UK's national institute for data science.
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.

Prior to my move to Oxford I was a Research Fellow in Computer Science at St Catharine's College at the University of Cambridge. I obtained my PhD from the Cambridge machine learning group, working with Prof Zoubin Ghahramani and funded by the Google Europe Doctoral Fellowship. Prior to that I studied at Oxford Computer Science department for a Master's degree under the supervision of Prof Phil Blunsom. Before my MSc I worked as a software engineer for 3 years at IDesia Biometrics developing code and UI for mobile platforms, and did my undergraduate in mathematics and computer science at the Open University in Israel.

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.