Professor
David Clifton
MEng DPhil (Oxon)
Royal Academy of Engineering Chair of Clinical Machine Learning
Email:
Tel: 01865 617708
College: Reuben College
Location: Institute of Biomedical Engineering, Old Road Campus Research Building, Oxford OX3 7DQ

Professor David Clifton is the Royal Academy of Engineering Chair of Clinical Machine Learning at the University of Oxford, and leads the Computational Health Informatics (CHI) Lab which focuses on “AI for Healthcare”.  He is also NIHR Research Professor, appointed as the first non-medical scientist to the NIHR’s “flagship chair”.

He is OCC Fellow in AI & ML at Reuben College, a Research Fellow of the Royal Academy of Engineering, Fellow of the Alan Turing Institute, Visiting Chair in AI for Health 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 Professor Lionel Tarassenko CBE. 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. His research has been commercialised via university spin-out companies OBS Medical, Oxehealth, Biobeats, and Sensyne Health, in addition to collaboration with multinational industrial bodies.

In 2018, the CHI Lab opened its second site, in the Oxford University-owned research labs in Suzhou (China), which focuses on open research in “Digital Health” using public data.  In 2019, the Wellcome Trust’s first “Flagship Centre” was announced, which joins CHI Lab to the Oxford University Clinical Research Unit in Vietnam, focused on AI for healthcare in low-income countries.  In 2021, the Oxford-CityU Centre for Cardiovascular Engineering was opened in Hong Kong, of which he is co-director.  In 2022, the Pandemic Sciences Institute opened at Oxford, of which he is an investigator and where CHI Lab provides its AI theme.

His research has won over 40 awards; he is a Grand Challenge awardee from the UK Engineering and Physical Sciences Research Council, which is an EPSRC Fellowship that provides long-term strategic support for nine “future leaders in healthcare.” 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.  He was the recipient of the IEEE Early Career Award in 2022, given to one engineer annually for achievements within the first ten years of their academic career.  He has previously taught widely across Oxford undergraduate and graduate courses in mathematics, statistics, and machine learning.

Research in the Computational Health Information (CHI) Laboratory focuses on the development of real in-hospital and in-home systems for AI-driven interventions that are used in practice. Translation into low- and middle-income countries (LMICs) is a parallel research theme.  The lab has a focus on non-imaging AI methods: time-series analysis, natural language processing, -omics data, and sensor informatics.

Please see our lab research page for details.

CHI Lab offers a wide range of complex, real-world projects in AI for healthcare.  Please contact Professor Clifton for enquiries.

Most doctoral students in the CHI Lab hold highly competitive scholarships (Rhodes, Clarendon, EU, etc.) for which Professor Clifton is happy to offer advice.

Adversarial de-confounding in individualised treatment effects estimation
Kumar V,  Molaei S,  Hoque Tania M,  Thakur A,  Zhu T,  Clifton D,  et al. (2023)
Retrieve, reason, and refine: generating accurate and faithful patient instructions
Liu F,  Yang B,  You C,  Wu X,  Ge S,  Liu Z,  Sun X,  Yang Y,  Clifton D,  et al. (2023)
Expectation-maximization contrastive learning for compact video-and-language representations
Jin P,  Huang J,  Liu F,  Wu X,  Ge S,  Song G,  Clifton D,  Chen J,  et al. (2023)
Privacy-aware early detection of COVID-19 through adversarial training
Rohanian M,  Kouchaki S,  Soltan A,  Yang J,  Yang Y,  Clifton D,  et al. (2023)
Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes
Lu H,  Ding X,  Hirst J,  Yang Y,  Yang J,  Mackillop L,  Clifton D,  et al. (2023)
Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England
Hill NR,  Groves L,  Dickerson C,  Ochs A,  Pang D,  Lawton S,  Hurst M,  Pollock KG,  Sugrue DM,  Tsang C,  Arden C,  Wyn Davies D,  Martin AC,  Sandler B,  Gordon J,  Farooqui U,  Clifton D,  Mallen C,  Rogers J,  Camm AJ,  Cohen AT,  et al. (2023)
Self-aware SGD: reliable incremental adaptation framework for clinical AI models
Thakur A,  Armstrong J,  Youssef A,  Eyre D,  Clifton DA,  et al. (2023)
Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank
Small S,  Chan S,  Walmsley R,  Fritsch LV,  Acquah A,  Mertes G,  Feakins B,  Creagh A,  Strange A,  Matthews C,  Clifton D,  Price A,  Khalid S,  Bennett D,  Doherty A,  et al. (2023)