Le Zhang bio photo

Le Zhang

Assistant Professor in Machine Learning/AI for Digital Healthcare, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham.

Honorary Research Fellow at William Harvey Research Institute, Barts Heart Center, Barts Health NHS Trust, Queen Mary University of London.

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About

Dr. Le Zhang is an Assistant Professor at the School of Engineering, College of Engineering and Physical Sciences in the University of Birmingham. He was a Postdoc Researcher at the University of Oxford since 2022. Before that, he was a Research Fellow at University College London since 2019 working with Prof. Daniel Alexander, Prof. Olga Ciccarelli and Prof. Frederik Barkhof. Under the supervision of Prof. Alejandro F Frangi, he obtained his Ph.D. in Medical Image Computing from the University of Sheffield in 2019.

His research is focused on computational modelling, pattern recognition, and machine learning for biomedical imaging and data analysis. His research aims to add understanding and capability in biomedicine by drawing on ideas from medical imaging, computer vision, data science, and machine learning. Much of his work focusses on Cardiac Magnetic Resonance Image Analysis, Multi-Modality Neuroimage Analysis, and Retinal Fundus Image Analysis. His research has been published in top conferences and journals in the fields of machine learning and medical image analysis, including NeurIPS, MICCAI, Medical Image Analysis and Pattern Recognition, and nominated for the MICCAI Young Scientist Award in 2019.

News

  • [05/2026] One paper accepted to PLOS Digital Health.
  • [03/2026] Horizon Europe Development Grant awarded from UoB.
  • [02/2026] 300,000 GPUh on the Dawn AIRR service awarded from UKRI.

Opening

I am looking for PhD students, Research Master students (project only) and Visiting Scholars with strong motivation to work on the following research topics, especially PhD students with CSC’s support potentially. Please feel free to drop an email (l.zhang.16@bham.ac.uk) if you are interested!

Research Topics

Medical Image Analysis and GenAI. This research area focuses on advanced medical image analysis using 3D deep learning, multimodal representation learning, and generative AI. It aims to extract clinically meaningful information from complex medical imaging data, including cardiac MRI, neuroimaging, retinal imaging, and multi-organ imaging. The goal is to develop robust, interpretable, and scalable AI models for disease diagnosis, risk prediction, and treatment planning.
Medical Large Vision Language Models. This research direction focuses on large language models and multimodal foundation models for medical and healthcare applications. It explores how LLMs can support clinical documentation, medical image interpretation, research automation, and intelligent decision support. Particular emphasis is placed on combining language, vision, and structured clinical data to build trustworthy AI systems for personalised and efficient healthcare.
Surgical Robot Intelligence. This research direction focuses on intelligent surgical robotics, including autonomous surgical systems, surgical video analysis, and vision-motor perception. It explores AI methods for understanding spatio-temporal patterns in surgical procedures, modelling surgical workflows, and supporting safe robotic decision-making. The aim is to enhance surgical precision, automation, and human-robot collaboration in complex clinical environments.
Embodied Intelligence and Medical AI. This direction investigates embodied AI systems for healthcare, particularly safe and reliable intelligent agents in medical environments. It explores the integration of perception, reasoning, and action to support clinical workflows, medical robotics, and interactive decision-making. It also includes the development of clinical decision-support agents that can reason over multimodal patient data while maintaining safety, transparency, and clinical relevance.