Tingting Zhu
BEng DPhil (Oxon) MSc
Associate Professor in AI for Digital Health
Non-Tutorial Fellow at Kellogg College
Royal Academy of Engineering Fellow
Stipendiary College Lecturer at Mansfield College
College: Kellogg College, Mansfield College

Professor Tingting Zhu graduated with the DPhil degree in information and biomedical engineering at Oxford University in 2016. This followed her MSc in Biomedical Engineering at University College London and BEng (Hons) in Electrical Engineering from the University of Malta.

After DPhil, Tingting was awarded a Stipendiary Junior Research Fellowship at St. Hilda’s College, Oxford. In 2018, Tingting was appointed as the first Associate Member of Faculty at the Department of Engineering Science; in 2019, following the award of her Royal Academy of Engineering Research Fellowship, she was appointed to full Member of Faculty at the Department of Engineering Science. Tingting is a Non-Tutorial Fellow at Kellogg College and a Stipendiary College Lecturer at Mansfield College.

Tingting’s 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 the developing world.

  • Machine learning for improving decision-making with telemedicine
  • Prognosis and diagnosis of adversarial events in multimorbid population
  • Dynamic modelling for understanding the impact of interventions on the hospital system
  • Phenotyping patients with complex diseases via electronic patient information
  • Machine learning for early cancer detection as well as treatment response
  • Digital twin and its application in healthcare
  • Modelling of treatment effect and treatment recommendation

Prof Zhu offers a wide range of machine learning projects for healthcare in both developed and developing countries. Prospective DPhil students should get in touch indicating their interest.


Adversarial de-confounding in individualised treatment effects estimation
Kumar V,  Molaei S,  Hoque Tania M,  Thakur A,  Zhu T,  Clifton D,  et al. (2023)
RapiD_AI: A framework for Rapidly Deployable AI for novel disease & pandemic preparedness
Youssef A,  Zhu T,  Thakur A,  Watkinson P,  Horby P,  Eyre D,  Clifton D,  et al. (2023)
Heterogeneity in the diagnosis and prognosis of ischemic stroke subtypes: 9-year follow-up of 22,000 cases in Chinese adults
Chun M,  Qin H,  Turnbull I,  Sansome S,  Gilbert S,  Hacker A,  Wright N,  Zhu T,  Clifton D,  Bennett D,  Guo Y,  Pei P,  Lv J,  Yu C,  Yang L,  Li L,  Lu Y,  Chen Z,  Cairns BJ,  Chen Y,  Clarke R,  et al. (2023)
Uncertainties in the analysis of heart rate variability: a systematic review
Lu L,  Zhu T,  Morelli D,  Creagh A,  Liu Z,  Yang J,  Liu F,  Zhang Y-T,  Clifton D,  et al. (2023)
Scalable federated learning for emergency care using low cost microcomputing: Real-world, privacy preserving development and evaluation of a COVID-19 screening test in UK hospitals
Soltan A,  Thakur A,  Yang J,  Chauhan A,  D’Cruz L,  Dickson P,  Soltan M,  Thickett D,  Eyre D,  Zhu T,  Clifton D,  et al. (2023)
Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention
Zhu T,  Armstrong J,  Abrol V,  Wang Y,  Clifton D,  Thakur A,  et al. (2023)
A Large Language Modelling Deep Learning Framework for the Next Pandemic
Zhu T,  Wu X,  Yang B,  You C,  Wang C,  Lu L,  Liu Z,  Zheng Y,  Sun X,  Yang Y,  Clifton D,  Liu F,  et al. (2023)