David Clifton
Professor of Clinical Machine Learning
David Clifton is Professor of Clinical Machine Learning in the Department of Engineering Science of the University of Oxford. He is a Research Fellow of the Royal Academy of Engineering, Visiting Chair in AI for Healthcare at the University of Manchester, and a Fellow of Fudan University, China. He runs the Computational Health Informatics Lab within the Department of Engineering Science, which has sites in Oxford (at the Institute of Biomedical Engineering) and China (in the Oxford Suzhou Centre for Advanced Research).
David studied Information Engineering at Oxford's Department of Engineering Science. His previous research resulted in patented systems for jet-engine health monitoring, used with the engines of the Airbus A380, the Boeing 787 "Dreamliner", and the Eurofighter Typhoon. Since 2008, he has focused mostly on healthcare applications, and his current research focuses on the development of machine learning for tracking the health of complex systems. Patents arising from his collaborative research have been commercialised via university spin-out companies OBS Medical, Oxehealth, and Sensyne Health. He holds an EPSRC "Grand Challenge" fellowship for "future leaders in healthcare", and was jointly awarded the inaugural Vice-Chancellor's Prize for Innovation, for interdisciplinary research.
Recent publications
Neuro-Symbolic Federated Learning over Heterogeneous Data-Views: A Structured Approach to Distributive EHR Modelling
Conference paper
Molaei S. et al, (2026), Proceedings of the AAAI Conference on Artificial Intelligence, 40, 24422 - 24430
Learning Across the Divide: Personalised Federated Learning for Robust Clinical Modelling Under Data-View Heterogeneity
Journal article
Molaei S. et al, (2026), IEEE Journal of Biomedical and Health Informatics, 30, 2000 - 2009
Digital morphine: why AI scribes are symptomatic relief for a broken system
Journal article
Segal B. et al, (2026), BMJ Digital Health & AI, 2, e000030 - e000030
Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics.
Journal article
Molaei S. et al, (2026), Journal of the American Chemical Society, 148, 10934 - 10944
Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals
Journal article
Gu X. et al, (2026), Nature Machine Intelligence, 8, 220 - 233