Summary
Taiyu Zhu is a Research Fellow at University of Oxford. He has completed his PhD at the Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London in 2022, where he currently holds a Visiting Researcher position. He was supported by Imperial College President's PhD Scholarship.
He has graduated with a First-Class Honours BEng degree from the Australian National University in 2017 and a Distinction MSc degree in Electrical and Electronic Engineering from Imperial College London in 2018. He received the Outstanding Achievement Award for his achievements in the MSc courses. He was awarded the Stylianos Kalaitzis PhD Award, the most promising doctoral work, in 2022.
His research focuses on artificial intelligence (AI) in healthcare. He has been working on developing novel machine learning and deep learning algorithms to meet the challenges in diabetes management. His research aims to deliver frontier biomedical engineering applications and AI-powered tools to improve the health and well-being for people with chronic diseases and solve real-world healthcare problems.
Publications
Journals
Zhu T, Kuang L, Piao C, et al., 2024, Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge., Ieee Trans Biomed Circuits Syst, Vol:18, Pages:236-246
Zhu T, Li K, Georgiou P, 2023, Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes., Ieee J Biomed Health Inform, Vol:27, Pages:5087-5098
Zhu T, Li K, Herrero P, et al., 2023, GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks., Ieee J Biomed Health Inform, Vol:27, Pages:5122-5133
Noaro G, Zhu T, Cappon G, et al., 2023, A Personalized and Adaptive Insulin Bolus Calculator Based on Double Deep Q- Learning to Improve Type 1 Diabetes Management, Ieee Journal of Biomedical and Health Informatics, Vol:27, ISSN:2168-2194, Pages:2536-2544
Zhu T, Kuang L, Daniels J, et al., 2023, IoMT-Enabled Real-Time Blood Glucose Prediction With Deep Learning and Edge Computing, Ieee Internet of Things Journal, Vol:10, ISSN:2327-4662, Pages:3706-3719