Hong Kong Polytechnic University
School of Optometry, Hong Kong Polytechnic UniversityI am a second-year Ph.D. student in the School of Optometry at The Hong Kong Polytechnic University, supervised by Prof. Danli SHI and co-supervised by Prof. Mingguang HE. Before that, I received an M.Eng. degree in Electronic Information from the Institute of Computing Technology, Chinese Academy of Sciences, where I was supervised by Prof. Yiqiang CHEN and Prof. Xinlong JIANG. I also received a B.Eng. degree in Software Engineering from Hainan University.
Research Interests:
⚡ Medical Agents and Agentic AI: AI agents for healthcare, autonomous medical systems, and intelligent clinical decision support.
Please feel free to contact me via email if you are interested in my research!
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Hong Kong Polytechnic UniversityPh.D. Student in the School of OptometrySep. 2025 - present
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Institute of Computing Technology, Chinese Academy of SciencesM.Eng. in Electronic InformationSep. 2022 - Jun. 2025 -
Hainan UniversityB.Eng. in Software EngineeringSep. 2018 - Jun. 2022
Honors & Awards
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Institute of Computing Technology Ziqiang International Exchange Scholarship2025
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National Scholarship (3%)2024
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First-Class Scholarship of ICT, CAS2024
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Outstanding Graduate of Hainan University2022
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Awards in various innovation and entrepreneurship competitions2019-2022
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First-Class Scholarship of Hainan University2019
Experience
- Institute of AI Industry Research, Tsinghua UniversityResearch InternJul. 2024 - Oct. 2024
- Hong Kong Baptist UniversityResearch AssistantJun. 2024 - Jul. 2024
- FedML Inc.Remote Research InternJun. 2022 - Sep. 2022
Academic Services
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ReviewerMICCAI, AAAI2025 - Present
Extracurricular Leadership & Service
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IEEE Hainan University BranchPresidentMar. 2021 - Jun. 2022
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Association of Robotics and Artificial IntelligenceCo-Founder & Vice PresidentJul. 2020 - Jun. 2022
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Cyberspace Security Association @Hainan UniversityVice PresidentSep. 2020 - Jun. 2021
News (view all )
Selected Publications (view all )
Buffalo: Biomedical Vision-Language Understanding with Cross-Modal Prototype and Federated Foundation Model Collaboration
Bingjie YAN, Qian CHEN, Yiqiang CHEN†, Xinlong JIANG, Wuliang HUANG, Bingyu WANG, Zhirui WANG, Chenlong GAO, Teng ZHANG († corresponding author )
ACM CIKM'24, CCF-B, CORE-A (Acceptance Rate: 22.7%) (2024) Oral
Buffalo addresses missing and heterogeneous modalities in collaborative biomedical learning with federated foundation models. Its cross-modal prototype imputation method improves downstream medical report generation and visual question answering without sharing raw client data.
Buffalo: Biomedical Vision-Language Understanding with Cross-Modal Prototype and Federated Foundation Model Collaboration
Bingjie YAN, Qian CHEN, Yiqiang CHEN†, Xinlong JIANG, Wuliang HUANG, Bingyu WANG, Zhirui WANG, Chenlong GAO, Teng ZHANG († corresponding author )
ACM CIKM'24, CCF-B, CORE-A (Acceptance Rate: 22.7%) (2024) Oral
Buffalo addresses missing and heterogeneous modalities in collaborative biomedical learning with federated foundation models. Its cross-modal prototype imputation method improves downstream medical report generation and visual question answering without sharing raw client data.
Model Trip: Enhancing Privacy and Fairness in Model Fusion across Multi-Federations for Trustworthy Global Healthcare
Qian CHEN, Yiqiang CHEN†, Bingjie YAN, Xinlong JIANG, Xiaojin ZHANG, Yan KANG, Teng ZHANG, Wuliang HUANG, Chenlong GAO, Lixin FAN, Qiang YANG († corresponding author )
ICDE'24, CCF-A (2024) Oral
This work introduces FairFusion, a cross-federation model-fusion method that optimizes utility, privacy, and fairness without additional training. Experiments on three healthcare datasets evaluate its performance across model structures and subgroups.
Model Trip: Enhancing Privacy and Fairness in Model Fusion across Multi-Federations for Trustworthy Global Healthcare
Qian CHEN, Yiqiang CHEN†, Bingjie YAN, Xinlong JIANG, Xiaojin ZHANG, Yan KANG, Teng ZHANG, Wuliang HUANG, Chenlong GAO, Lixin FAN, Qiang YANG († corresponding author )
ICDE'24, CCF-A (2024) Oral
This work introduces FairFusion, a cross-federation model-fusion method that optimizes utility, privacy, and fairness without additional training. Experiments on three healthcare datasets evaluate its performance across model structures and subgroups.
FedEYE: A Scalable and Flexible End-to-end Federated Learning Platform for Ophthalmology
Bingjie YAN*, Danmin CAO*, Xinlong JIANG, Yiqiang CHEN†, Weiwei DAI†, Fan DONG, Wuliang HUANG, Teng ZHANG, Chenlong GAO, Qian CHEN, Zhen YAN, Zhirui WANG (* equal contribution, † corresponding author )
Patterns, Cell Press, JCR-Q1, IF=6.7 (2024)
FedEYE is an end-to-end federated-learning platform that lets ophthalmologists create and deploy collaborative machine-learning projects without programming. It supports flexible deployment while keeping clinical data decentralized.
FedEYE: A Scalable and Flexible End-to-end Federated Learning Platform for Ophthalmology
Bingjie YAN*, Danmin CAO*, Xinlong JIANG, Yiqiang CHEN†, Weiwei DAI†, Fan DONG, Wuliang HUANG, Teng ZHANG, Chenlong GAO, Qian CHEN, Zhen YAN, Zhirui WANG (* equal contribution, † corresponding author )
Patterns, Cell Press, JCR-Q1, IF=6.7 (2024)
FedEYE is an end-to-end federated-learning platform that lets ophthalmologists create and deploy collaborative machine-learning projects without programming. It supports flexible deployment while keeping clinical data decentralized.