Dr Tingting Zhu

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


Dr Tingting Zhu is a Junior Research Fellow in Engineering at St Hilda's College.


University of Oxford: Departmental Tutor            2016-to date

Teaching third-year undergraduate tutorials in information engineering (B14 probabilistic inference).


Tingting's current research focuses on machine learning for global health, with specific emphasis on chronic diseases.  Her research investigates how statistical methods can be used for understanding complex patient data acquired from low-cost devices in a resource-constrained setting. Tingting has also previously developed algorithms for online, unsupervised learning that utilises crowd-sourced medical data.

As a senior researcher in the Computational Health Informatics Lab at the Institute of Biomedical Engineering led by Prof. David Clifton, Tingting’s work involves investigating the development of Bayesian methods for phenotyping patients, with a special emphasis on haemodialysis studies in collaboration with Prof. Chris Pugh of the Nuffield Department of Medicine. This research is funded by the National Institute for Health Research and the Engineering and Physical Sciences Research Council.

Given Tingting's interest in global health, she is currently the principal investigator (PI) for a project that looks to improving access to high-quality health care in the Philippines, where the doctor:patient ratios are 1:20,000, through low-cost medical devices using machine-learning techniques. This project is in collaboration with the National Health Centre, Philippines and supported by the Royal Academy of Engineering Frontiers of Engineering for Development Award.

Tingting has also been awarded an EPSRC NetworksPlus prize as a PI to work on the next generation of mhealth applications with clinicians in Guangzhou (including overseeing a proof-of-principle study of 20,000 patients in one of China’s leading cardiovascular hospitals), working with the George Institute for Global Health.


Book Chapters

[1] T. Zhu, et al. A Bayesian Fusion Model for fusing Biomedical Labels. Machine Learning for Healthcare Technologies. IET, 2016.

Journal Articles

[1] T. Zhu, et al. Modelling Patient-Specific Trajectory using Hierarchical Bayesian Gaussian Processes. IEEE Transactions on Biomedical Engineering (2017). [In review]

[2] T. Zhu, et al. Bayesian Fusion of Physiological Measurements using Signal Quality Extensions. Physiological Measurement (2017). [In review]

[3] T. Zhu, et al. Approximate Bayesian Inference for Fusing Weak Learners in Biosignal Analysis. The IEEE Journal of Biomedical and Health Informatics (2017). [In review]

[4] Y. Shen, Y. Yang, T. Zhu, et al. Risk Prediction for Cardiovascular Disease using Electrocardiogram Data in the China Kadoorie Biobank. Annals of Biomedical Engineering (2017). [In review]

[5] F. Shamout, L. Clifton, T. Zhu, et al. Age-And Sex-Based Early Warning Score. Critical Care Medicine (2017). [In review]

[6] 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.

[7] 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.

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

[9] A. Dizem, T. Zhu, et al. Optimal wavelength combinations for near-infrared spectroscopic monitoring of changes in brain tissue hemoglobin and cytochrome c oxidase concentrations. Biomedical Optics Express 6, no. 3 (2015): 933-947.

[10] 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.

[11] R. R. Bond, P. D. Kligfield, T. Zhu, et al. Novel approach to documenting expert ECG interpretation using eye tracking technology: A historical and biographical representation of the late Dr Rory Childers in action. Journal of Electrocardiology 48, no. 1 (2014): 43-44.

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

[13] A. Bainbridge, ..., T. Zhu, et al. Brain Mitochondrial Oxidative Metabolism During and After Cerebral Hypoxia-Ischemia Studied by Simultaneous Phosphorus Magnetic-Resonance and Broadband Near-Infrared Spectroscopy. Neuroimage 102 (2014): 173-183.

[14] A. Snowball, ..., T. Zhu, and R. C. Kadosh. Long-Term Enhancement of Brain Function and Cognition Using Cognitive Training and Brain Stimulation. Current Biology 23, no. 11 (2013): 987-992.


Conference Papers (peer-reviewed)

[1] T. Zhu, et al. Unsupervised Bayesian Inference for Fusing Sensory Estimates in Physiological Time-series Analysis. NIPS WiML Workshop, 2017. [In Review]

[2] T. Zhu, et al. Real-time Signal Quality Diagnostics for Low-Cost ECG Telehealth in the Developing World. MEIBioeng, 2017. [In Press]

[3] T. Zhu, et al. Personalised Patient Monitoring in Haemodialysis Using Hierarchical Gaussian Processes. IEEE Engineering in Medicine and Biology Society, 2017. [In press]

[4] G.W. Colopy, T. Zhu, et al. Likelihood-based artefact detection in continuously-acquired patient vital signs. IEEE Engineering in Medicine and Biology Society, 2017. [in press]

[5] T. Zhu, et al. Respiratory Rate Estimation from the Photoplethysmogram using a Bayesian Fusion Model. IET Appropriate Healthcare Technologies, 2016. [In press]

[6] T. Zhu, et al. Bayesian Fusion of Algorithms for the Robust Estimation of Respiratory Rate from the Photoplethysmogram. IEEE Engineering in Medicine and Biology Society pp. 6138-6141, 2015.

[7] T. Zhu, et al. Crowdsourced annotation of EMG onset times in healthy individuals and Parkinson disease. International Society for Posture and Gait Research World Congress, 2015.

[8] T. Zhu, et al. CrowdLabel: A Crowd-sourcing Platform for Electrophysiology. Computing in Cardiology Conference, 2014.

[9] T. Zhu, et al. An Intelligent Cardiac Health Monitoring and Review System. IET Appropriate Healthcare

Technologies, pp. 1-4, 2014.

[10] G. D. Clifford, C. Arteta, T. Zhu, et al. A Scalable mHealth System for Noncommunicable Disease Management. IEEE Global Humanitarian Technology Conference, 2014.

[11] J. Behar, T. Zhu, et al. Evaluation of the foetal QT interval using non-invasive fetal ECG technology, The 34th Annual Meeting of the Society for Maternal-Fetal Medicine: The Pregnancy Meeting, 2014.

[12] T. Zhu, et al. Bayesian Voting of Multiple Annotators for Improved QT Interval Estimation. Computing in Cardiology Conference, pp.659- 662, 2013.

[13] I. Silva, ..., T. Zhu, et al. Noninvasive Fetal ECG: the PhysioNet/Computing in Cardiology Challenge 2013. Computing in Cardiology Conference, pp.149 -152, 2013.

[14] T. Zhu, et al. Optimal Wavelength Combinations for Resolving In-vivo Concentration Changes of Haemoglobin and Cytochrome-coxidase with fNIRS - a pre-clinical study. Functional Near Infrared Spectroscopy Conference, 2012.

[15] T. Zhu, et al. Optimal Wavelength Combinations for Resolving in-vivo Changes of Haemoglobin and Cytochrome-c-oxidase Concentrations with NIRS. Biomedical Optics and 3-D Imaging, Optical Society of America Technical Digest, paper JM3A.6, 2012.

[16] A. Snowball, ..., T. Zhu, et al. Enhancing Mathematical Learning: Concurrent Non-invasive Brain Stimulation and Optical Imaging. Annual Cognitive Neuroscience Society Meeting, 2012.

[17] T. Zhu, et al. Monitoring brain oxygenation and metabolism in the adult during frontal lobe functional activation using a novel seven wavelength near-infrared spectrometer. International Symposium on Cerebral Blood Flow, Metabolism, and Function, 2011.