About Me

I will be moving to the University of Oxford to take the position of Associate Professor of Machine Learning at the Computer Science department this coming October. I will also be 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 admissions website.
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.

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.


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.

ICML updates


Publications page has been updated with two accepted ICML papers [1, 2], as well as lots of other fun stuff.

New blog post and thesis (Uncertainty in Deep Learning)


I added a new blog post about uncertainty in deep learning, which reviews lots of bits and pieces of research I had lying around (along-side my PhD thesis that I recently submitted).

NIPS papers and Bayesian Deep Learning workshop


Publications page has been updated with NIPS papers. We're also organising a Bayesian Deep Learning workshop at NIPS 2016.

Research interests

My interests lie in the fields of linguistics, applied maths, and computer science. Most of my work is motivated by problems found in the intersections of these fields, with a major theme being understanding empirically developed machine learning techniques. At the moment, I develop Bayesian techniques for deep learning, with applications to reinforcement learning. In the past I worked on Bayesian modelling, Gaussian processes and BNP, approximate inference, natural language processing, and much more. A list of publications is available here.

Curriculum vitae

My Résumé is available here.

Contact me


yg279 -at- cam.ac.uk


Cambridge University
Engineering Department
Cambridge, CB2 1PZ
United Kingdom