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Publications

We do research in both theoretical machine learning, as well as applications of machine learning in various domains, including medical, astronomy, autonomous driving, and more.

Recent publications:
08/2022: "Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions" (Tomáš Gavenčiak*, Joshua Teperowsky Monrad*, Gavin Leech, Mrinank Sharma, Sören Mindermann, Samir Bhatt, Jan Brauner, Jan Kulveit*).
08/2022: "Bayesian uncertainty quantification for machine-learned models in physics" (Yarin Gal, Petros Koumoutsakos, Francois Lanusse, Gilles Louppe, Costas Papadimitriou).
07/2022: "Interlocking Backpropagation; Improving depthwise model-parallelism" (Aidan Gomez, Oscar Key, Kuba Perlin, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal).

Reproducibility and Code

One of our lab's missions is to contribute to the reproducibility effort. Here we provide code for our own research, as well as reproductions of works by others (e.g. ones we implemented as baselines as part of our own research).

Recent code release:
05/2022: Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval (Pascal Notin).
12/2021: Improving black-box optimization in VAE latent space using decoder uncertainty (Pascal Notin).
11/2021: Disease variant prediction with deep generative models of evolutionary data (Pascal Notin).

Group Members

We are researchers coming from varied backgrounds, including Computer Science, Maths & Stats, Engineering, and Physics.

We come from academia (Oxford, Cambridge, MILA, Manchester, U of Amsterdam, U of Toronto, U of Cape Town, Yale, and others) and industry (Google, DeepMind, Twitter, Qualcomm, and startups).

We include 5 Rhodes Scholars, 3 Clarendon Scholars, 3 DeepMind Scholars, one cancer research UK Scholar, with our students funded by many additional sources (AIMS CDT, Cyber CDT, industry, and more). If you'd like to join us take a look here.

Current group members: Yarin Gal (Associate Professor) ● Freddie Kalaitzis (Senior Research Fellow, 2020) ● Sebastian Farquhar (Postdoc, 2017) ● Gunshi Gupta (PhD, 2021) ● Kelsey Doerksen (PhD, 2021) ● Lorenz Kuhn (PhD, 2021) ● Lars Holdijk (MSc by Research, 2021) ● Jannik Kossen (PhD, 2020) ● Lisa Schut (PhD, 2020) ● Muhammed Razzak (PhD, 2020) ● Tuan Nguyen (Associate Member (PhD), 2020) ● Gabriel Jones (Associate Member (PhD), 2020) ● Freddie Bickford Smith (Associate Member (PhD), 2020) ● Angus Nicolson (Associate Member (PhD), 2020) ● Atılım Güneş Baydin (Associate Member (Faculty), 2020) ● Neil Band (MSc by Research, 2020) ● Andrew Jesson (PhD, 2019) ● Jan Brauner (PhD, 2019) ● Pascal Notin (PhD, 2019) ● Sören Mindermann (PhD, 2019) ● Jishnu Mukhoti (Associate Member (PhD), 2019) ● Tom Rainforth (Associate Member (Faculty), 2019) ● Angelos Filos (PhD, 2018) ● Aidan Gomez (PhD, 2018) ● Andreas Kirsch (PhD, 2018) ● Clare Lyle (PhD, 2018) ● Joost van Amersfoort (PhD, 2018) ● Panagiotis Tigas (PhD, 2018) ● Milad Alizadeh (PhD, 2017) ● Tim G. J. Rudner (PhD, 2017)


Group Invited Talks

Recent invited talks:
02 Nov 2021, 15:00, Zoom Owain Evans (Oxford).
18 Oct 2021, 15:00, Oxford Stanislav Fort (Oxford).
06 Jul 2021, 15:00, Zoom Pavel Izmailov (NYU).
16 Jun 2021, 15:00, Zoom Tamara Broderick (MIT).

Group Collaborators and Affiliates

Our group collaborators include:



And our group affiliates include:

Are you looking to do a PhD in machine learning? Did you do a PhD in another field and want to do a postdoc in machine learning? Would you like to visit the group?

How to apply


Contact

We are located at
Department of Computer Science, University of Oxford
Wolfson Building
Parks Road
OXFORD
OX1 3QD
UK
Twitter: @OATML_Oxford
Github: OATML
Email: oatml@cs.ox.ac.uk