Final Call for Application | OxML 2025

[OxML Team]

Join us this summer for the Oxford Machine Learning Summer School (OxML) at the University of Oxford’s Mathematical Institute and online.

·       MLx Health & Bio: 2–5 August 2025

·       MLx Representation Learning & Generative AI: 7–10 August 2025

 

 Website: www.oxfordml.school

Application Deadline: 07 July 2025

 

 

About OxML 2025

The Oxford Machine Learning Summer School (OxML), organised by AI for Global Goals, brings together top researchers and industry leaders to explore the latest advances in machine learning. This year’s programme features two expert tracks, each blending theory with real-world application:

 

 

1.    MLx Health & Bio

A deep dive into ML methods for healthcare and biomedical research. Topics include medical imaging, EHR, multi-omics, drug discovery, wearable devices, computational biology, and more.

Confirmed Speakers & Topics:

·       Michael Bronstein (Oxford): Geometric deep learning for drug discovery

·       Arthur Gretton (UCL, DeepMind): Causal ML

·       Aiden Doherty (Oxford): Wearables & ML for health outcomes

·       Zeyu Gao (Cambridge): Multiomics

·       Hoifung Poon (Microsoft Health): Foundation models in medicine

·       Brian Hie (Stanford): Biological foundation models (Evo 2)

·       Rahul Krishnan (Toronto): Time-to-event modeling

·       Vivek Natarajan (Google): Multimodal foundation models in biomedicine

·       Pierre Masselot (LSHTM): ML in environmental epidemiology

·       Moritz Kraemer (Oxford): ML for pandemics

 

2.    MLx Representation Learning & Generative AI

This track focuses on the theoretical and applied aspects of representation learning and generative AI, including LLMs, CV, RL, multi-modal models, AI alignment, GenAI product development, and more.

Confirmed Speakers & Topics:

·       Aymeric Dieuleveut (École Polytechnique): Conformal prediction

·       Peyman Milanfar (Google): Denoising in imaging & ML

·       Cheng Zhang (Meta): TBC

·       Abdul Fatir Ansari (AWS): Time series representation learning

·       Ashley Edwards (Runway): Generative AI in images/videos

·       Fazl Barez (Oxford): AI safety & alignment

·       Haitham Bou-Ammar (Huawei): TBC

·       Gerhard Neumann (KIT): TBC

·       Hannaneh Hajishirzi (UW, AI2): TBC

·       David Salinas (UW): Tabular foundation models, AutoML

·       Edward Johns (Imperial): GenAI for robotics

·       Ilia Shumailov (DeepMind): ML security

 

3.    Practical Workshops (included in both tracks)

Hands-on sessions on deploying ML to edge devices and robots.

Open-Source Tools for ML on Edge Devices & Robots:

·       Vincent Moens (Meta)

·       Rémi Cadène (Hugging Face)

·       Xuan-Son Nguyen (Hugging Face)

Deploying CV Applications to Embedded Systems:

·       Mergen Nachin (Meta)

 

We welcome applications from PhD students, postdocs, researchers, and professionals in ML and related disciplines.