About
Chris is a Senior Enterprise Fellow within the IT Innovation Centre, as part of the Department of Electronics and Computer Science (ECS) at the University of Southampton. He is also a member of the Digital Health and Biomedical Engineering Research Group and has held honorary positions within the NHS, including University Hospitals Southampton Foundation Trust since 2022.
Chris is currently seconded to the NIHR. He is the Applied Research Delivery Lead within the Data and Technology Theme of the NIHR Applied Research Collaboration Wessex. In this role, he supports applied research across health and care systems, with a focus on data, digital technology, artificial intelligence, systems integration, evaluation, and implementation. His work in ARC Wessex is closely aligned with regional and national priorities around neighbourhood health, integrated care, and the responsible use of data-driven technologies in real-world services.
Chris received his PhD in Theoretical Astrophysics from the University of St Andrews (2020) and has previously been a fellow at the Flatiron Institute (New York) and a visiting researcher at the European Southern Observatory (ESO) and the Max Planck Institute for Astronomy (Heidelberg). Chris joined the University of Southampton in 2021, switching focus to Digital Health.
Chris’ research focusses on the application of artificial intelligence and machine learning into the real-world. Despite a technical background covering statistics, explainable machine learning (XAI), data drift and natural language processing, he is interested in socio-technological problem solving through co-design. His work actively involves end-users (i.e., NHS staff, patients and public) in the development, evaluation and implementation of machine learning systems to ensure they are usable, trustworthy and aligned with clinical and lived experience.
Chris works directly with companies and the NHS to consider how data and AI can be leveraged and ultimately, implemented effectively. He currently leads the PROCED-DST project which considers how machine learning can reduce delays in hospital discharge and the ACCESS project which is investigating how large-language models can be adapted to help patients decide on technology for type-1 diabetes. He is also CO-I on a number of projects using machine learning as decision support for the NHS and patients living with long-term conditions.
Chris is also a member of the NIHR Southampton Biomedical Research Centre (Data, Health and Society). He has authored 25+ papers across Digital Health, Astronomy and Finance (10,000+ citations).