Bio: Patrick Rebeschini is Statutory Professor of Statistical Science at the University of Oxford and a Professorial Fellow at St Anne’s College. His research combines probability, statistics and optimisation to develop efficient and statistically robust methods for machine learning and artificial intelligence. His work spans learning theory, algorithmic stability, implicit regularisation, reinforcement learning and the theory of generative models.

He is Director of the Oxford ELLIS Unit and Deputy Head of Oxford’s Department of Statistics. His distinctions include an ERC Consolidator Grant, an Amazon Research Award (2025) and Oxford’s MPLS Teaching Award (2019). He has served as Senior Area Chair for ICML and Area Chair for COLT and NeurIPS. He obtained his PhD from Princeton University and held research and teaching positions at Yale University before joining Oxford in 2017.

Bio: Judith Rousseau is Professor of Statistics at CEREMADE, Paris Dauphine University–PSL, and is affiliated with the University of Oxford. Her research spans Bayesian theory and methodology, particularly the frequentist properties of Bayesian procedures and the connections between Bayesian uncertainty quantification, machine learning and generative models. She also works on MCMC and related algorithms, and the elicitation of subjective prior distributions.

She has held numerous leadership roles in learned societies and is currently President-Elect of the International Society for Bayesian Analysis (ISBA). A Fellow of ISBA and the IMS, she received the Ethel Newbold Prize in 2015 and an ERC Advanced Grant in 2019. She delivered a Medallion Lecture in 2017 and the Le Cam Lecture in 2025, and was invited to give a sectional lecture at the International Congress of Mathematicians in 2026.

Bio: Eloisa Bentivegna is a Senior Research Scientist at IBM Research UK and a UKRI Future Leaders Fellow. Her research combines computational physics, high performance computing and artificial intelligence to model complex physical systems and identify extreme phenomena. Her work spans scientific machine learning and uncertainty quantification, including the use of foundation models for weather and climate applications.

She obtained her PhD in Physics from Penn State University, with a minor in High Performance Computing. Before joining IBM, she held a Marie Curie Fellowship at the Max Planck Institute for Gravitational Physics and a National Montalcini Fellowship and faculty position at the University of Catania. Her earlier research addressed numerical relativity, strong gravity and quantum fields. She has also contributed to the Einstein Toolkit, a major open source software framework for numerical relativity.

Bio: Neil Lawrence is the DeepMind Professor of Machine Learning at the University of Cambridge and academic lead of ai@cam, the University’s flagship AI initiative. His research spans probabilistic machine learning, uncertainty quantification and the design of reliable AI systems, with particular emphasis on scientific applications and the practical challenges of deployment.

He previously served as Director of Machine Learning at Amazon and holds a Senior AI Fellowship at the Alan Turing Institute. He has served as Programme Chair and General Chair of NeurIPS, and is the founding editor of the Proceedings of Machine Learning Research and a co-founder of Data Science Africa. He has also served on the UK’s AI Council and is the author of The Atomic Human, which explores human intelligence and the societal implications of AI.