Biography
Dr 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 BSc in Electrical Engineering from the University of Malta.
Dr Zhu's DPhil focussed on the development of probabilistic techniques for combining information from wearable sensors to form a consensus that provides accurate monitoring of time-series medical data. After DPhil, Dr Zhu was awarded a Stipendiary Junior Research Fellowship at St. Hilda's College, Oxford.
In 2018, Dr Zhu 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.
Most Recent Publications
Incremental Trainable Parameter Selection in Deep Neural Networks.
Thakur A, Abrol V, Sharma P, Zhu T & Clifton DA (2022), IEEE transactions on neural networks and learning systems, PP
Improving classification of tetanus severity for patients in low-middle income countries wearing ECG sensors by using a CNN-transformer network
Lu P, Wang C, Hagenah J, Ghiasi S, Zhu T et al. (2022), IEEE Transactions on Biomedical Engineering
Adversarial de-confounding in individualised treatment effects estimation
Kumar V, Molaei S, Hoque Tania M, Thakur A, Zhu T et al. (2022), arXiv
Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing.
Lu P, Ghiasi S, Hagenah J, Hai HB, Hao NV et al. (2022), Sensors (Basel), 22(17)
Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.
Ghiasi S, Zhu T, Lu P, Hagenah J, Khanh PNQ et al. (2022), Sensors (Basel, Switzerland), 22(10), 3866
Research Interests
Dr Zhu’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.
Current Projects
- Machine learning for improving decision-making with telemedicine
- Prognosis and diagnosis of adversarial events in multimorbid population and monitoring of longitudinal treatment in multimorbid patients in both developed and developing countries
- Dynamic modelling for understanding the impact of interventions on the hospital system
- Phenotyping patients with complex diseases via electronic medical records
- Machine learning for early cancer detection as well as patient treatment response
Most Recent Publications
Incremental Trainable Parameter Selection in Deep Neural Networks.
Thakur A, Abrol V, Sharma P, Zhu T & Clifton DA (2022), IEEE transactions on neural networks and learning systems, PP
Improving classification of tetanus severity for patients in low-middle income countries wearing ECG sensors by using a CNN-transformer network
Lu P, Wang C, Hagenah J, Ghiasi S, Zhu T et al. (2022), IEEE Transactions on Biomedical Engineering
Adversarial de-confounding in individualised treatment effects estimation
Kumar V, Molaei S, Hoque Tania M, Thakur A, Zhu T et al. (2022), arXiv
Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing.
Lu P, Ghiasi S, Hagenah J, Hai HB, Hao NV et al. (2022), Sensors (Basel), 22(17)
Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.
Ghiasi S, Zhu T, Lu P, Hagenah J, Khanh PNQ et al. (2022), Sensors (Basel, Switzerland), 22(10), 3866
DPhil Opportunities
Dr 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.
Most Recent Publications
Incremental Trainable Parameter Selection in Deep Neural Networks.
Thakur A, Abrol V, Sharma P, Zhu T & Clifton DA (2022), IEEE transactions on neural networks and learning systems, PP
Improving classification of tetanus severity for patients in low-middle income countries wearing ECG sensors by using a CNN-transformer network
Lu P, Wang C, Hagenah J, Ghiasi S, Zhu T et al. (2022), IEEE Transactions on Biomedical Engineering
Adversarial de-confounding in individualised treatment effects estimation
Kumar V, Molaei S, Hoque Tania M, Thakur A, Zhu T et al. (2022), arXiv
Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing.
Lu P, Ghiasi S, Hagenah J, Hai HB, Hao NV et al. (2022), Sensors (Basel), 22(17)
Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.
Ghiasi S, Zhu T, Lu P, Hagenah J, Khanh PNQ et al. (2022), Sensors (Basel, Switzerland), 22(10), 3866
