Imperial College London

MrTaiyuZhu

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Postgraduate
 
 
 
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Contact

 

taiyu.zhu17

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Summary

Taiyu Zhu is a PhD candidate at the Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London. Currently he is 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, Li K, Herrero P, et al., 2022, Personalized blood glucose prediction for Type 1 diabetes using evidential deep learning and meta-learning., Ieee Transactions on Biomedical Engineering, ISSN:0018-9294

Zhu T, Uduku C, Li K, et al., 2022, Enhancing self-management in type 1 diabetes with wearables and deep learning, Npj Digital Medicine, Vol:5, ISSN:2398-6352

Zhu T, Kuang L, Daniels J, et al., 2022, IoMT-Enabled Real-time Blood Glucose Prediction with Deep Learning and Edge Computing, Ieee Internet of Things Journal

Zhu T, Li K, Herrero P, et al., 2021, Deep Learning for Diabetes: A Systematic Review, Ieee Journal of Biomedical and Health Informatics, Vol:25, ISSN:2168-2194, Pages:2744-2757

Conference

Zhu T, Li K, Herrero P, et al., 2022, RECURRENT GENERATIVE ADVERSARIAL NETWORKS FOR GLUCOSE TIME SERIES GENERATION, MARY ANN LIEBERT, INC, Pages:A229-A229, ISSN:1520-9156

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