OxML 2026 — Final Speaker Line-up & Limited Remaining Places

[Oxford Machine Learning (OxML)]

10–18 July 2026 | Oxford Mathematical Institute & Online

OxML 2026 is approaching, and we are sharing the final programme updates for this year’s school. 

A limited number of Oxford in-person places remain available on a first-come, first-served basis.

The programme brings together researchers and practitioners working across machine learning, healthcare, biology, generative AI, multimodal learning, robotics, causal inference, AI safety, and AI for mathematics.

Participants may attend either in Oxford or online.

MLx Health & Bio (10–13 July)

Topics include EHR learning, digital health and novel data modalities, sensor and foundation models, geometric deep learning, biomedical imaging, real-world data, epidemiology, and cellular-scale biology.

Confirmed speakers include:

• Gaël Varoquaux (INRIA)
• Demilade Adedinsewo (Mayo Clinic)
• Joe Futoma (Apple)
• Michael Bronstein (University of Oxford / AITHYRA)
• Sarah Teichmann (University of Cambridge)
• Blanca Rodríguez (University of Oxford)
• Christopher Yau (University of Oxford)
• Ines Prata Machado (University of Cambridge)
• Shishir Rao (University of Oxford)
• Emma Robinson (King’s College London)

MLx Representation Learning & Generative AI (15–18 July)

Topics include AI for mathematics, intelligent data gathering, multimodal AI, multi-agent systems, causal discovery, geometric deep learning, generative modelling, embodied intelligence, and AI safety.

Confirmed speakers include:

• Kevin Buzzard (Imperial College London)
• Tom Rainforth (University of Oxford)
• Amanda Prorok (University of Cambridge)
• Ricardo Silva (UCL)
• Paul Liang (MIT)
• Peter Gostev (Arena AI)
• Fazl Barez (University of Oxford)
• Tony Feng (UC Berkeley / Google DeepMind)
• Petar Veličković (Google DeepMind)
• Tim Rocktäschel (UCL)
• Alexander Tong (AITHYRA)

Register here: www.oxfordml.school/register

Full-time students and participants from LMICs are eligible for a 40% registration fee discount.

 

OxML Team

con...@oxfordml.school

www.oxfordml.school