Invited Speakers
We are proud to be hosting the following exemplary speakers.
John Humphrey Plummer Professor of Machine Learning, AI and Medicine. She is founder and director of the Cambridge Centre for AI in Medicine, an IEEE Fellow, and a Fellow of the Royal Society (2024). Her research develops machine learning methods for dynamic, real-world decision-making, with a strong emphasis on uncertainty quantification, adaptive clinical trials, and data-centric AI. She brings perspectives on principled evaluation and adaptation under distribution shift that complement the workshop's theoretical agenda, particularly around valid inference and the limits of post-training under changing conditions.
University of Oxford / Google DeepMind
Professor of Statistical Machine Learning at Oxford and Research Scientist at Google DeepMind, and co-director of an ELLIS programme on Robust Machine Learning. His research spans probabilistic learning, meta-learning, Bayesian nonparametrics, and uncertainty quantification. Recent work on non-stationary learning of neural networks, online adaptation of language models, and continual learning addresses the core challenge of post-training under evolving conditions. He delivered the Breiman Lecture on Bayesian Deep Learning at NeurIPS 2017.
University of Groningen
Assistant Professor in the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen, Gido works at the intersection of machine learning, artificial intelligence and cognitive science, with a particular focus on continual learning. He is also interested in using insights and intuitions from neuroscience to make the behaviour of deep neural networks more human-like. Since receiving his PhD in Neuroscience from the University of Oxford, he has held a Marie Skłodowska-Curie Fellowship at KU Leuven. Before this, he was a postdoctoral researcher at Baylor College of Medicine and a visiting researcher at the University of Cambridge.
Assistant Professor and head of the ALPI Lab (UZH), affiliated with the ETH AI Center. She works on safe and robust reinforcement learning in non-stationary settings, multi-agent learning, and imitation learning. Her research on learning from human feedback in multi-agent and bilevel settings, and on safety under task evolution, connects directly to the workshop's emphasis on robustness, feedback loops, and safety-relevant failure modes. She was previously a postdoctoral researcher at the ETH AI Center, advised by Andreas Krause and Niao He.
Associate Professor and Turing Fellow. She leads the Cooperative AI Lab at KCL, working on reinforcement learning, multi-agent systems, and scalable alignment. Her research on reward design, learning under uncertainty, and human–AI coordination directly addresses the workshop’s interest in preference feedback modelling and adaptive data collection. Recent work on LLM training via verifier-free RL and evaluating generalisation in LLM-based agents connects directly to the workshop’s focus on post-training under evolving feedback.