Biography

I am currently a post-doctoral fellow at the Department of Biomedical Informatics, Harvard Medical School, where I am working with Prof. Pranav Rajpurkar. I lead the ReXplain project, which aims to bridging AI for patient-centered understanding. I also co-lead the Medical AI Bootcamp (for Harvard, Stanford, and MIT students and medical doctors all around the world). I have also had pleasure working with Prof. Hao Chen at HKUST. I obtained my Ph.D. degree from the CSE department, CUHK, advised by Prof. HENG Pheng-Ann and Prof. WONG, Tien-Tsin. Previously, I obtained my B.Sc. degree from the CSE department, CUHK.


My research interest lies broadly in the intersection of AI and healthcare. Currently, I am particularly interested in developing and applying foundation models for multimodal biomedical data analysis to bridging AI, patients, and doctors.

Harvard Initiatives

ReXplain

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Medical AI Bootcamp

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Rajpurkar Lab

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News

[07/2025] We released the ReXGrounding-CT dataset! Check the preprint here.
[06/2025] One paper on benchmarking text-prompted medical segmentation models was accepted by MLHC 2025.
[05/2025] We are hosting the VLM3D Challenge ! Special thanks to Ibrahim for putting this together.
[05/2025] Our ReXplain work is featured at SIIM
[05/2025] One paper early accepted by MICCAI
[05/2025] One paper accepted by TMI.
[04/2025] One paper accepted by MedIA.
[03/2025] We are hosting the Deep-Brea3th 2025 Workshop with MICCAI .
[02/2025] Nominated a member of Sigma Xi, The Scientific Research Honor Society .
[01/2025] One abstract was selected as Oral Presentation at SIIM 2025 .
[01/2025] One paper got accepted by Medical Image Analysis.
[01/2025] One paper got accepted by Nature Communications!
[01/2025] Invited to serve as Area Chair for MICCAI 2025!
[01/2025] Our paper ReXplain won the Best Paper of AI for Medicine and Healthcare, AAAI Bridge Program 2025!
[11/2024] Our Editorial on Trustworthy Machine Learning for Health Informatics is published!
[10/2024] One paper on scale-aware segmentation of lesions from medical images was accepted by IEEE TNNLS.
[09/2024] Awarded the Second Place in the Poster Presentation category at the 2024 Harvard DBMI Science Day.
[07/2024] Serve as a guest editor for the CMIG SI on TAI4MI .
[07/2024] Survey on AI-based breast cancer imaging was selected as a featured article for IEEE RBME.
[05/2024] Two papers were early accepted by MICCAI 2024.
[01/2024] One survey on Deep Learning in Breast Cancer Imaging was accepted by IEEE RBME.
[01/2024] Our paper on multimodal and explainable AI for cervical cancer diagnosis was accepted by Nature Communications.
[01/2024] One paper on Omni-supervised Learning was accepted by TMI.
[12/2023] One paper on XAI for skin lesion was accepted by AAAI 2024.
[08/2023] Proceedings of TML4H 2023 was officially online.
[06/2023] Two papers were accepted by MICCAI 2023, one of which was early accepted.
[05/2023] One paper on MIL-based WSI classification was accepted by IEEE TMI.
[05/2023] Serve as the program committee of the IJCAI 2024 workshop on TAI4H .
[05/2023] I have been serving as a guest editor for the IEEE JBHI SI on Trustworthy Machine Learning for Health Informatics .