Miles Cranmer is Assistant Professor in Data Intensive Science at the University of Cambridge, joint between the Department of Applied Mathematics and Theoretical Physics and the Institute of Astronomy. He received his PhD from Princeton University, spending time at Google DeepMind and Flatiron Institute, and before that, his BSc from McGill University. Miles is interested in automating scientific research in the physical sciences with machine learning, and works on a variety of pure and applied machine learning projects in pursuit of this goal. His ML research has concentrated on symbolic regression, graph neural networks, and physics-motivated architectures, while his applied projects have looked at multi-scale physics, planetary dynamics, and cosmology.
Urban Fasel is a lecturer in the Department of Aeronautics at Imperial College London. His research interests range from co-design optimization and machine learning for modeling and control of complex systems to adaptive structures and autonomous flight systems. Before joining Imperial, Urban was a Postdoc in Mechanical Engineering with the University of Washington, mentored by Steve Brunton, and closely collaborating with Bing Brunton and Nathan Kutz. Urban received his Doctor of Science in Mechanical Engineering at ETH Zurich, developing co-design optimization and data-driven control methods for morphing wings applied to Airborne Wind Energy.