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Professor James Fergusson is a theoretical cosmologist and award-winning university lecturer at the Department of Applied Mathematics and Theoretical Physics. He is the Executive Director of the Data Intensive Science programmes at the University of Cambridge, and Director of the Infosys Cambridge Enterprise AI Centre.

His research group operates at the interface of physics and artificial intelligence, focusing on areas where AI has the potential to fundamentally transform the way science is conducted. Key research themes include AI for simulations, using physics to deepen our understanding of AI, employing AI to explore equations, advancing AI interpretability and error quantification, and developing agentic automation for scientific discovery.

James frequently delivers talks and workshops on the application of AI in science and business, delivers lectures on the impact of AI via the Judge business school, and his work has been featured in the Financial Times.

Professor Alexander Archibald is Professor of Atmospheric Chemistry at the University of Cambridge, where he leads the Atmospheric Chemistry Modelling group, and is head of the National Centre for Atmospheric Science at Cambridge University. His work leverages a hierarchy of models to understand the interactions between atmospheric composition (the gases and aerosols in the atmosphere) and the climate system. This work increasingly spans space and time with current research questions focused around the use of Earth based models to understand the atmospheric chemistry on distant exoplanets and help answer the outstanding question of whether or not we are alone in the Universe.  A recent focus of his research is developing and applying AI based models to speed up climate models and specific processes within them. He seeks to leverage these models and the growing wealth of observational data to reduce spread in future estimates and better characterise the risks of future extreme conditions for air quality and climate related change. 

Dr Matthew Osman is an Assistant Professor of Climate Science at the University of Cambridge, where he leads the Cambridge Computational Climatology and Paleoceanography (C3PO) research group. His work combines climate models with historical and geological records of the past --- including sediments, ice cores, and other environmental archives --- to understand how Earth’s climate system responds to major changes in temperature, greenhouse gases, and ice sheets. A key focus of his research is developing statistical frameworks that allow climate models to “learn” from past climates. By combining the “big data” of Earth’s geologic past with model simulations, he aims to reduce uncertainties in projections of future climate change.

Dr Shijing Sun is an Associate Professor at the Department of Materials Science & Metallurgy, University of Cambridge, leading the Autonomous Materials Group. She read Natural Sciences at Trinity College, Cambridge, and completed her PhD under Prof. Anthony Cheetham before postdoctoral research at MIT with Prof. Tonio Buonassisi. She later became a Research Scientist at MIT, leading high-throughput synthesis and characterisation for perovskite photovoltaics and optoelectronics. Before returning to academia, she worked at the Toyota Research Institute as a Senior Research Scientist, focusing on the application of AI to battery and fuel cell research. She was also an Associate Editor for APL Machine Learning from 2023 - 2025.