Tsinghua University School of Biomedical Engineering | Tsinghua Medicine
Research Interests
Medical Artificial Intelligence
Intelligent Health Systems
Medical Imaging Computing
Multimodal Fusion Modeling
Medical Large Language Models
Self-Supervised Learning
Intelligent Health Ecosystems
Professional Affiliations
- Assistant Professor & Doctoral Supervisor, School of Biomedical Engineering, Tsinghua University
- Founding Member & Head of AI Systems, a2z Radiology AI
- Research Scientist, Tencent Medical AI Lab
Academic Honors
- World's Top 2% Scientist by Stanford University (2024)
- Featured by the U.S. National Academy of Sciences
Professional Experience
2024
Ph.D. in Computer Science, The University of Hong Kong (2024)
Ph.D. in Computer Science, The University of Hong Kong (2024)
Research Scientist, Tencent Medical AI Lab (2 years)
Research Scientist, Tencent Medical AI Lab (2 years)
Founding Member & Head of AI Systems, a2z Radiology AI
Founding Member & Head of AI Systems, a2z Radiology AI
2025
Assistant Professor, School of Biomedical Engineering, Tsinghua University (2025-present)
Assistant Professor, School of Biomedical Engineering, Tsinghua University (2025-present)
Biography
Dr. Hong-Yu Zhou joined the School of Biomedical Engineering at Tsinghua University as a tenure-track assistant professor in 2025. He received his Ph.D. in Computer Science from the University of Hong Kong in 2024 and conducted postdoctoral research at Harvard Medical School. In industry, he was a founding member and head of AI systems at a2z Radiology AI and a research scientist at Tencent's Medical AI Lab. His research focuses on building scalable, trustworthy, and clinically impactful medical AI systems. He has made significant contributions to medical imaging, multimodal fusion, and medical large language models, with over 40 publications, 4700+ citations, and an h-index of 34. He was named a 'World's Top 2% Scientist' by Stanford University in 2024.
Awards & Recognition
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Publications & Representative Works
Complete Bibliography
2026
Yang, H., Zhou, H. Y., et al. (2026). AFLoc: A multimodal vision-language model for generalizable annotation-free pathology localization. Nature Biomedical Engineering.
2026
Zhou, H. Y., Rodman, A., Liu, P., et al. (2026). Large reasoning models as thinking machines for medicine. NEJM AI.
2023
Zhou, H. Y., Lian, C., Wang, L., & Yu, Y. (2023). Advancing Radiograph Representation Learning with Masked Record Modeling. In ICLR 2023.
2023
Zhou, H. Y., Lu, C., Chen, C., et al. (2023). A Unified Visual Information Preservation Framework for Self-supervised Pre-Training in Medical Image Analysis. IEEE TPAMI, 45, 8020-8035.
2023
Zhou, H. Y., Yu, Y., Wang, C., et al. (2023). A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. Nature Biomedical Engineering, 7, 743-755.
2021
Zhou, H. Y., Lu, C., Yang, S., Han, X., & Yu, Y. (2021). Preservational learning improves self-supervised medical image models by reconstructing diverse contexts. In ICCV 2021.
2021
Zhou, H. Y., Chen, X., Zhang, Y., et al. (2021). Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports. Nature Machine Intelligence, 4, 32-40.
