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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:
09/2025: "Reasoning Introduces New Poisoning Attacks Yet Makes Them More Complicated" (Hanna Foerster, Ilia Shumailov, Yiren Zhao, Harsh Chaudhari, Jamie Hayes, Robert Mullins, Yarin Gal).
08/2025: "Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs" (Kyle O'Brien, Stephen Casper, Quentin Anthony, Tomek Korbak, Robert Kirk, Xander Davies, Ishan Mishra, Geoffrey Irving, Yarin Gal, Stella Biderman).
07/2025: "Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition" (Andy Zou, Maxwell Lin, Eliot Jones, Micha Nowak, Mateusz Dziemian, Nick Winter, Alexander Grattan, Valent Nathanael, Ayla Croft, Xander Davies, Jai Patel, Robert Kirk, Nate Burnikell, Yarin Gal, Dan Hendrycks, J. Zico Kolter, Matt Fredrikson).

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:
07/2025: Rethinking aleatoric and epistemic uncertainty (Freddie Bickford Smith).
07/2024: AI models collapse when trained on recursively generated data (Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, Yarin Gal).
06/2024: Detecting hallucinations in large language models using semantic entropy (Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, Yarin Gal).
04/2023: Bayesian active learning with EPIG data acquisition (Freddie Bickford Smith).
05/2022: Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval (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).

Current and former members include Rhodes Scholars, Clarendon Scholars, 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) ● Lin Li (Postdoc, 2025) ● Tiarnan Doherty (Postdoc, 2022) ● Daniella (Zihuiwen) Ye (PhD, 2024) ● Xander Davies (PhD, 2024) ● Yoav Gelberg (PhD, 2024) ● Suhaas Bhat (Associate Member (PhD), 2024) ● Luckeciano Carvalho Melo (PhD, 2023) ● Matthew Kearney (PhD, 2023) ● Yonatan Gideoni (PhD, 2023) ● Hazel Kim (Associate Member (PhD), 2023) ● Katrina Dickson (Associate Member (Program Manager), 2023) ● Ilia Shumailov (Associate Member (Senior Research Fellow), 2022) ● Gunshi Gupta (PhD, 2021) ● Kelsey Doerksen (PhD, 2021) ● Ruben Weitzman (PhD, 2021) ● Shreshth Malik (PhD, 2021) ● Muhammed Razzak (PhD, 2020) ● Freddie Bickford Smith (Associate Member (PhD), 2020) ● Gabriel Jones (Associate Member (Faculty), 2020) ● Atılım Güneş Baydin (Associate Member (Faculty), 2020) ● Pascal Notin (Associate Member (Senior Research Fellow), 2019) ● Tom Rainforth (Associate Member (Faculty), 2019) ● Sebastian Farquhar (Associate Member (Senior Research Fellow), 2017)


Group Invited Talks

Recent invited talks:
20 Jun 2025, Katie Collins, Cambridge (The Study and Design of Human-AI Thought Partnerships).
19 Jun 2025, Prof Alison Gopnik, University of California Berkeley (Intrinsically-motivated humans and agents in open-world exploration).
18 Jun 2025, Gregory Lau, National University of Singapore (Tackling Data-Centric Challenges of Large AI Systems: Provenance Unlearning and Uncertainty).
11 Jun 2024, Marco Sertoli, Tokamak Energy (ML for Fusion Research).

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