Christopher Bishop
Technical Fellow, Microsoft Research AI for Science
Chris Bishop is a Microsoft Technical Fellow and the founder of Microsoft Research AI for Science. He is also Honorary Professor of Computer Science at the University of Edinburgh, a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Chris was a founding member of the UK AI Council, and in 2019 he was appointed to the Prime Minister’s Council for Science and Technology.
He has a PhD in theoretical physics and worked for ten years on plasma physics for the fusion programme before transitioning into machine learning. Chris joined Microsoft in 1997 and was Lab Director of Microsoft Research Cambridge from 2015 until 2022 when he founded the new AI for Science team.
Chris is the author of the highly cited and widely adopted machine learning textbooks Neural Networks for Pattern Recognition (Oxford, 1995) and Pattern Recognition and Machine Learning (Springer, 2006). He recently published a new textbook Deep Learning: Foundations and Concepts which was recently confirmed as Springer-Nature’s top selling book of both 2024 and 2025.
Chris is also a keen amateur rocketry enthusiast and works with university teams on high-altitude student rocketry projects.
Nathan Kutz
Physics-Informed AI at Autodesk Research, London UK
Nathan Kutz is Director of Physics-Informed AI at Autodesk Research in London UK where he is leading a team that integrates model reduction techniques with machine learning and AI. He is on-leave from the University of Washington where he is the Boeing Professor of Applied Mathematics and Electrical and Computer Engineering and former Director of the AI Institute in Dynamic Systems. He received the BS degree in physics and mathematics from the University of Washington in 1990 and the Phd in applied mathematics from Northwestern University in 1994. He was a postdoc in the applied and computational mathematics program at Princeton University before taking his faculty position. He has a wide range of interests, including neuroscience to fluid dynamics where he integrates machine learning with dynamical systems and control.