Prof. David Clifton
CHI Lab Faculty
David A. Clifton is Professor of Clinical Machine Learning in the Department of Engineering Science of the University of Oxford, and OCC Fellow in AI & Machine Learning at Reuben College, 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 studied Information Engineering at Oxford's Department of Engineering Science, supervised by Prof. Lionel Tarassenko CBE, Chair of Electrical Engineering. His research focuses on the development of machine learning for tracking the health of complex systems. 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 the development of AI-based methods for healthcare. Patents arising from this collaborative research have been commercialised via university spin-out companies OBS Medical, Oxehealth, and Sensyne Health, in addition to collaboration with multinational industrial bodies.
Prof. Clifton teaches the undergraduate mathematics syllabus and biomedical syllabus in Engineering Science. He holds a Grand Challenge award from the UK Engineering and Physical Sciences Research Council, which is an EPSRC Fellowship that provides long-term strategic support for "future leaders in healthcare". His research has been awarded over 35 academic prizes; in 2018, he was joint winner of the inaugural "Vice-Chancellor's Innovation Prize", which identifies the best interdisciplinary research across the entirety of the University of Oxford.
Dr. Tingting Zhu
CHI Lab Faculty
Tingting Zhu is a Royal Academy of Engineering Research Fellow (equivalent to Assistant Professor) and Member of Faculty in the Department of Engineering Science. She is a Fellow of St. Hilda's College, Oxford and a Lecturer at Mansfield College, Oxford.
She graduated with the DPhil degree in information and biomedical engineering within the Institute of Biomedical Engineering at Oxford University, following degrees in Biomedical Engineering and Electrical Engineering. Her research interests lie in machine learning for healthcare applications and she has developed probabilistic techniques for reasoning about time-series medical data. Her work involves the development of machine learning for understanding complex patient data, with an emphasis on Bayesian inference, deep learning, and applications involving low-income countries.