Biography

Lei Li is an Assistant Professor at the Department of Biomedical Engineering, National University of Singapore (NUS) and PI of Digital Heart Lab (DHlab). Previously, she was a Lecturer at the School of Electronics & Computer Science, University of Southampton. She received her PhD degree from the School of Biomedical Engineering, Shanghai Jiao Tong University (SJTU) in 2021 and obtained the SJTU 2021 Outstanding Doctoral Graduate Development Scholarship. She is a Board Member of SIG-Cardiac and Women in MICCAI (WiM) and Area Chair of MICCAI 2024. She has been selected as the Rising Star of Women in Engineering by Asian Deans’ Forum 2023 and Featured Women in Science by RSIP Vision. Her research interest is AI for healthcare, specifically focusing on cardiac digital twins, medical imaging, and multi-modal AI.

📢📢📢 I am on looking for Postdoc and PhD students who are interested in AI for heathcare and digital twins. For more details, please check our DHlab website ✨.

Research/Education Experience

• 10/2024-now   Assistant Professor, Department of Biomedical Engineering, National University of Singapore, Singapore
• 01/2024-10/2024   Lecturer, School of Electronics & Computer Science, University of Southampton, Southampton, UK
• 08/2021-12/2023   Postdoctoral Research Assistant, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
• 09/2016-06/2021   PhD, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
• 09/2012-06/2016   Bachelor, Department of Medical Information Engineering, Sichuan University, Chengdu, China

Academic Services

• Editor:
  • Guest Associate Editor: IEEE Transactions on Medical Imaging (TMI).
  • Editorial Board Member: Journal of Medical Artificial Intelligence
  • Guest Editor: Special Issue of Journal of Imaging
• Board Member:
  • MICCAI Special Interest Group (SIG)-Cardiac
  • Women in MICCAI (WiM)
• Area Chair: MICCAI 2025; MICCAI 2024
• Organizer:
  • DT4H 2025 Workshop: International Workshop on Digital Twin for Healthcare
  • CARE 2024 Challenge: Comprehensive Analysis & computing of REal-world medical images
  • LAScarQS 2022 Challenge: Left Atrial and Scar Quantification & Segmentation
  • MyoPS 2020 Challenge: Myocardial Pathology Segmentation Combining Multi-sequence CMR
  • MS-CMRSeg 2019 Challenge: Multi-sequence Cardiac MR Segmentation
  • MM-WHS 2017 Challenge: Multi-modality Whole Heart Segmentation

Honors & Awards

• 2025: Ranked 1st place in MICCAI 2025 Dehazing Echocardiography Challenge
• 2024: IEEE TMI Distinguished Reviewer - Gold Level (2023-2024)
• 2024: 2nd Prize of Shanghai Science and Technology Award
• 2024: Featured Woman in Science at RSIP Vision
• 2023: The Rising Star of Women in Engineering - Asian Deans’ Forum 2023
• 2023: IEEE TMI Distinguished Reviewer - Gold Level (2022-2023)
• 2022: IEEE TMI Distinguished Reviewer - Bronze Level (2020-2022)
• 2022: SJTU 2021 Outstanding Doctoral Graduate Development Scholarship

Selected Invited Talks

• 2025-09-30   “AI Powered Cardiac Digital Twins”, at Yonsei University. (Seoul, South Korea)
• 2025-09-29   “Bridging the AI-Clinic Gap: From Models to Medicine”, at Chung-Ang University. (Seoul, South Korea)
• 2025-08-25   “Advanced Computer Vision for Medical Image Analysis and Diagnostics” at Healthcare AI Symposium 2025. (Singapore)
• 2025-07-20   “AI Powered Cardiac Digital Twins for Personalized Cardiac Arrhythmia Treatment”, in MICS-China. (Ningbo, China)
• 2025-03-24   “AI Powered Cardiac Digital Twins for Personalized Cardiac Arrhythmia Treatment”, in UK-ASEAN Workshop on Partnership in Health Technologies powered by Artificial Intelligence. (Hanoi, Vietnam)
• 2025-03-18   “AI Powered Cardiac Digital Twins for Personalized Cardiac Arrhythmia Treatment”, in World Health Organization (WHO) - Global Initiative on AI for Health (GI-AI4H) Workshop. (Singapore)
• 2025-03-07   “AI Powered Cardiac Digital Twins for Personalized Cardiac Arrhythmia Treatment”, in Machine Learning in Health Club (MLiHC) at University of New South Wales. (Online)
• 2024-06-03   “AI Powered Multi-Modal Cardiac Data Analysis: Towards to Cardiac Digital Twins for Personalized Healthcare”, in Isaac Newton Institute Workshop - Fickle Heart: The intersection of UQ, AI and Digital Twins. (Cambridge, UK)
• 2024-05-19   “Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference”, in Heart Rhythm 2024. (Boston, USA)
• 2024-04-23   AI powered cardiac digital twins: towards to personalized cardiac arrythmia treatment” at IBME.ai Healthcare Workshop. (Oxford, UK)
• 2023-07-18   “Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference”, in Multi-scale & Multi-modality Digital Health Collaboration Workshop, supported by the Alan Turing Institute. (London, UK)
• 2023-02-15   “Artificial Intelligence in Cardiac Image Computing and Modeling”, which is hosted by Prof. Evangelos B. Mazomenos at University College London. (London, UK)
• 2022-12-08   “Domain Generalization and Distributed Learning for Left Atrial Segmentation from Multi-Center LGE-MRI”, in Turing workshop on Human-AI Interaction in Bio-Medicine. (Oxford, UK)

Selected Publications

1. L Li, et al. “Personalized topology-informed localization of standard 12-Lead ECG electrode placement from incomplete cardiac MRIs for efficient cardiac digital twins.” Medical Image Analysis 101 (2025): 103472.
2. L Li, et al. “Solving the inverse problem of electrocardiography for cardiac digital twins: a survey.” IEEE Reviews in Biomedical Engineering 18 (2024): 316-336.
3. L Li, et al. “Towards enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference.” IEEE Transactions on Medical Imaging 43. no. 7 (2024): 2466-2478.
4. L Li, et al. “Multi-modality cardiac image computing: A survey.” Medical Image Analysis 88 (2023): 102869.
5. L Li, et al. “MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images.” Medical Image Analysis 87 (2023): 102808.
6. L Li, et al. “Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: a review.” Medical Image Analysis 77 (2022): 102360.
7. L Li, et al. “AtrialJSQnet: a new framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information.” Medical Image Analysis 76 (2022): 102303.
8. L Li, et al. “ AtrialGeneral: domain generalization for left atrial segmentation of multi-center LGE MRIs”, MICCAI, 557-566, 2021.
9. L Li, et al. “ Joint left atrial segmentation and scar quantification based on a DNN with spatial encoding and shape attention. “ MICCAI, 118-127, 2020.
10. L Li, et al. “Atrial scar quantification via multi-scale CNN in the graph-cuts framework.” Medical Image Analysis 60 (2020): 101595.