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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:
10/2021: "Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe" (Mrinank Sharma, Sören Mindermann, Charlie Rogers-Smith, Gavin Leech, Benedict Snodin, Janvi Ahuja, Jonas B. Sandbrink, Joshua Teperowsky Monrad, George Altman, Gurpreet Dhaliwal, Lukas Finnveden, Alexander John Norman, Sebastian B. Oehm, Julia Fabienne Sandkühler, Laurence Aitchison, Tomas Gavenciak, Thomas Mellan, Jan Kulveit, Leonid Chindelevitch, Seth Flaxman, Yarin Gal, Swapnil Mishra, Samir Bhatt, Jan Brauner).
10/2021: "Causal-BALD: Deep Bauesian Active Learning of Outcomes to Infer Treatment-Effects" (Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal).
10/2021: "Self-Consistent Models and Values" (Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado van Hasselt, David Silver).

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:
07/2020: OATomobile: A research framework for autonomous driving (Angelos Filos, Panagiotis Tigas).
07/2020: Slurm for Machine Learning (Joost van Amersfoort).
05/2020: Torch Memory-adaptive Algorithms (TOMA) (Andreas Kirsch).

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) ● Freddie Bickford Smith (PhD, 2020) ● Jannik Kossen (PhD, 2020) ● Lisa Schut (PhD, 2020) ● Muhammed Razzak (PhD, 2020) ● Tuan Nguyen (Associate Member (PhD), 2020) ● Gabriel Jones (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) ● Jishnu Mukhoti (PhD, 2019) ● Pascal Notin (PhD, 2019) ● Sören Mindermann (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) ● Binxin (Robin) Ru (PhD, 2018) ● Lewis Smith (PhD, 2017) ● Milad Alizadeh (PhD, 2017) ● Tim G. J. Rudner (PhD, 2017)

Group Invited Talks

Recent invited talks:
16 Mar 2021, 15:00, Zoom Debbie Marks (Harvard).
09 Mar 2021, 15:00, Zoom Greg Yang (Microsoft Research). Feature Learning in Infinite-Width Neural Networks
02 Mar 2021, 14:00, Zoom Uri Shalit (Technion - Israel Institute of Technology). Causality-Inspired Machine Learning
23 Feb 2021, 17:00, Zoom Adi Hanuka (SLAC National Laboratory, Stanford). Machine learning for Design and Control of Particle Accelerators

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


We are located at
Department of Computer Science, University of Oxford
Wolfson Building
Parks Road
Twitter: @OATML_Oxford
Github: OATML