Digital Health
Theme Lead: Mark Sujan
Chair in Safety Science
AI technologies have tremendous potential to revolutionise healthcare and help address the pressing challenges the NHS and health systems face, including financial constraints, workforce shortages, an aging population, and novel ways of treating diseases. However, these benefits will only be realised if we move away from a technology focus and consider AI as part of the wider socio-technical system. Safety issues will arise at the point where AI is integrated into clinical systems, and where people and technologies interact.
My research focuses on human-centred methods that enable us to understand and design the interactions of healthcare AI technologies within the wider system. This might include, for example, methods that help identify and evaluate the systemic risks associated with the widespread adoption of generative AI technologies. A significant part of this work is engagement with relevant stakeholders, such as the Health Services Safety Investigations Body and patient advocacy bodies, e.g., the charity Patient Safety Learning.
In communicating research to a broader non-academic audience I recently wrote an article for The Conversation.
Doctors are already using AI in care – but we don’t actually know what safe use should look like
Digital Humanities
Theme Lead: Dr. Mike Stuart
Lecturer, Department of Philosophy
Mike Stuart uses digital methods in the philosophical study of scientific methods. By bringing a variety of methods to bear on the study of method itself, he questions traditional assumptions about certain methods being most "natural" for certain disciplines, and investigates methods that are common to both the humanities and the sciences, including artificial intelligence, computer simulation, thought experiments, (data) visualizations, narrative, abstraction, and modelling. This work has been funded by the Social Sciences and Humanities Research Council of Canada, the Swiss National Science Foundation, and the National Science and Technology Council of Taiwan.
At York we have an exciting community of people using digital methods in the humanities, and questioning what it means to use those methods, and how to improve them. If you're interested, please email mike (dot) stuart (at) york (dot) ac (dot) uk to join the mailing list.
Machine Learning
Theme Lead: Dr. Nick Zachariou
Lecturer in Nuclear/Hadron Physics
As a dedicated researcher and lecturer specialising in Nuclear and Hadron Physics, my work lies at the intersection of advanced data-driven methodologies and the exploration of fundamental forces in the universe. I am passionate about applying cutting-edge data mining, machine learning, and AI-driven techniques to unravel the complexities of hadronic interactions. By leveraging these innovative approaches, my research not only aims to deepen our understanding of nuclear and hadronic physics but also to push the boundaries of current analytical methods. I am particularly focused on integrating AI and machine learning algorithms to develop novel ways of analysing large-scale experimental data, optimising detector technologies, and enhancing predictive modelling in complex physical systems. By placing data and AI at the forefront, I seek to pioneer new methodologies that can accelerate discoveries in fundamental physics and inform the design of next-generation experimental tools.
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