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Hi, I am a postdoctoral fellow at Harvard University in the Department of Biomedical Informatics, working with Prof. Pranav Rajpurkar. I received my PhD in Shanghai Jiao Tong Universiy, advised by Prof. Weidi Xie and Prof. Ya Zhang. I received my bachelor degree from School for the Gifted Young, University of Science and Technology of China in June 2019. My research interest focuses on Artificial Intelligence for Healthcare (AI4Health), with the ultimate goal of developing a generalist medical AI system. I am also intereated in AI for scientific discovery, with application across biology, chemistry, and medicine. Google Scholar / Github / LinkedIn / Twitter |
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Recent News
Congratulations to all co-authors on the following acceptances, and thank you for the excellent teamwork!
[03/2026] 1 paper has been accepted by Nature Medicine.
[03/2026] 1 paper has been accepted by MIDL 2026(Oral).
[02/2026] 1 paper has been accepted by Nature.
[12/2025] 1 paper has been accepted by JBHI.
[12/2025] 1 paper has been accepted by NEJM AI.
[11/2025] 1 paper has been accepted by Machine Learning for Health (ML4H) 2025.
[09/2025] 1 paper has been accepted by Radiology.
[09/2025] 3 papers have been accepted by Pacific Symposium on Biocomputing (PSB) 2026.
[09/2025] 2 abstracts have been accepted by 2025 RSNA Cutting-Edge Research Abstracts.
[09/2025] 1 paper has been accepted by Nature Scientific Data.
[08/2025] 1 paper has been accepted by npj Digital Medicine.
[06/2025] ReXVQA, a large-scale, high quality question answering benchmark for chest X-rays, is released.
[06/2025] 1 paper has been accepted by Nature Communications.
[05/2025] ReXGradient-160K, the largest Chest X-ray report generation dataset in terms of patient number, is released.
[04/2025] 2 papers have been accepted by CHIL 2025.
Datasets & Benchmarks
ReX-MLE: A medical ML benchmark for evaluating automated AI agents on realistic medical imaging tasks. ReXGradient-160K: A large-scale publicly available dataset of chest radiographs with free-text reports ReXVQA: A large-scale visual question answering benchmark for generalist chest X-ray understanding 3DReasonKnee: A 3D Reasoning Benchmark for Knee MRI ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports ReXRank: A Public Leaderboard for AI-Powered Radiology Report Generations RadGenome-ChestCT: A grounded vision-language dataset for chest CT analysis PMC-VQA: A large-scale, high quality question answering benchmark for chest X-rays
Selected Research
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Based on a template by Jon Barron.
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