Dr Tingting Zhu

BEng(Hons) (Melit), MSc (UCL), DPhil (Oxf)


Dr Zhu graduated with the DPhil degree in information engineering and biomedical engineering from the Department of Engineering Science at Oxford, in 2016, within the Centre for Doctoral Training in Healthcare Innovation. This followed her MSc in Biomedical Engineering at University College, London, and her BSc in Electrical Engineering from the University of Malta. After an EPSRC-funded postdoctoral research position in "AI for healthcare", Tingting was awarded a Stipendiary Junior Research Fellowship at St. Hilda's College. In 2018, Tingting was appointed as its first Associate Member of Faculty by the Department of Engineering Science, which is a scheme that seeks to recognise distinguished early-career researchers; in 2019, following the award of her highly competitive five-year Royal Academy of Engineering (RAEng) Research Fellowship, Tingting was appointed to full Member of Faculty as an independent academic.


Tingting’s interests lie in machine learning for healthcare applications; Her research involves the development of machine learning methodologies for understanding complex patient data, via Bayesian inference, deep learning, and applications involving the developing world. Her RAEng Engineering for Development Research Fellowship focuses on creating clinical artificial intelligent (AI) systems for tackling multimorbidities in resource-constrained settings, with the emphasis on phenotyping patients for risk stratification, polypharmacy and treatment strategies. Tingting is the principal investigator for grants in these areas awarded by the Royal Academy of Engineering, the EPSRC, the UK Global Challenges Research Fund, Cancer Research UK, and the National Institute for Health Research.


Book Chapter:

- T. Zhu, et alA Bayesian Fusion Model for fusing Biomedical Labels. Machine Learning for Healthcare Technologies. IET, 2016.

Selected Peer-reviewed Journal Publications:


- F.  Shamout, T. Zhu, et al. Deep Interpretable Early Warning System for the Prediction of Clinical Deterioration. IEEE Journal of Biomedical and Health Informatics (2019). [In press]

- J. Xiong, X. Liang, T. Zhu, et al. A New Physically Meaningful Threshold of Sample Entropy for Detecting Cardiovascular Diseases. Entropy 21, no. 9(2019): 830.

- T. Zhu, et al. Patient-Specific Physiological Monitoring using Structured Gaussian Processes. IEEE Access 7(2019): 58094 – 58103.

- Y. Xie, J. Li, T. Zhu, and C. Liu. Continuous-Valued Annotations Aggregation for Heart Rate Detection. IEEE Access, 7(2019): 37664 – 37671.

- Y. Yang, …, T. Zhu, D. A. Clifton and Cryptic Consortium, 2019. DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis. Bioinformatics 35, no.18, (2019): 3240 – 3249.

- T. Zhu, et al. Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalising Care. IEEE Journal of Biomedical and Health Informatics 23, no. 1 (2019): 47 – 58.

- T. Zhu, et al. Bayesian Fusion of Physiological Measurements using Signal Quality Extensions. Physiological Measurement 39, no.6 (2018): 065008.

- Y. Yang, …, T. Zhu, and D. A. Clifton. Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data. Bioinformatics 34, no.10 (2018): 1666 – 1671.

- J. Behar, T. Zhu, et al. Evaluation of the Fetal QT Interval Using Non-Invasive Fetal ECG Technology. Physiological Measurement 37, no.9 (2016): 1392.

- C. Breen, T. Zhu, et al.  The Evaluation of an Open Source Online Training System for Teaching 12 Lead Electrocardiographic Interpretation. Journal of Electrocardiology 49, no.3 (2016): 454 – 461.

- T. Zhu, et al. Fusing Continuous-Valued Medical Labels Using a Bayesian Model. Ann. Biomedical Engineering 43, no.12 (2015):2892 – 2902.

- R. R. Bond, T. Zhu, et al. Assessing computerized eye tracking technology for gaining insight into expert interpretation of the 12-lead electrocardiogram: an objective quantitative approach. Journal of Electrocardiology 47, no. 6 (2014): 895 – 906.

- T. Zhu, et al.  Crowd-sourced Annotation of ECG Signals Using Contextual Information. Annals of Biomedical Engineering 42, no. 4 (2014): 871 – 884.