Bin Zhang
Ph.D., Principal Investigator
Email: zhangbin@@gdiist.cn
Personal Profile:
Dr. Bin Zhang is the Principal Investigator of the Lab of Data‑Intelligence Life Fusion Computing. He received his PhD degree from South China University of Technology in 2022, supervised by Prof. Hongmin Cai. Supported by the China Scholarship Council (CSC), he conducted joint PhD training at Kyoto University, Japan in 2022 under the supervision of Prof. Tatsuya Akutsu. From 2023 to 2026, he worked as a postdoctoral researcher at the Guangdong Institute of Intelligence Science and Technology, with Prof. Luonan Chen as his supervisor. His research interests include high-dimensional few-shot learning, generative data modeling, multimodal data fusion and biomedical applications. In the past five years, he has published more than 30 SCI journal papers and nearly 10 international conference papers. Among them, 10 papers were published as the first or corresponding author in journals such as Nat. Commun., IEEE TPAMI, IEEE TCYB and IEEE TMM. He holds 4 authorized invention patents and participated in technology transfer worth 30 million RMB. He has led 3 research projects, including a sub-project of the Major Research Plan of the National Natural Science Foundation of China (NSFC), the NSFC Young Scientists Foundation (Category C), and the General Program of China Postdoctoral Science Foundation. He has also participated in 5 NSFC projects including the Key Supported Project of the NSFC Joint Fund and General Programs.
Lab of Data‑Intelligence Life Fusion Computing
The research group focuses on fundamental theories and applied research of intelligent computing in life science and biomedicine. We are committed to developing a multi-source heterogeneous data fusion computing framework driven by both multimodal data and artificial intelligence models. Our research topics include high-dimensional few-shot learning, generative data modeling, multimodal data fusion and related methodologies. Leveraging digital intelligence as the core approach, the group integrates multi-source heterogeneous data such as medical images and life omics data to address critical challenges of biomedical data: high dimensionality, scarcity of labeled samples, modality heterogeneity and insufficient model interpretability. Balancing fundamental algorithm innovation and biomedical application validation, we establish the pipeline for fusion modeling of multi-source life data. Our work provides novel computational tools and theoretical support for life science research, and promotes the practical deployment of artificial intelligence algorithms in life science and clinical diagnosis.
Open Positions
The group has long-term openings for postdocs, joint PhD students, research assistants and research interns. For detailed recruitment information, please check the official WeChat account of the Institute.
Feel free to contact me via email: zhangbin@gdiist.cn
Selected Publications：
1. Zhang, B., Tao, P., Ren, J., Wei, J., Wang, Q., Cai, S., ... & Chen, L*. (2026). Explainable data generation from observed small samples by matrix tri-factorization. Nature Communications, DOI: 10.1038/s41467-026-77343-0.
2. Zhang, B., Zhang, Y., Li, J., Chen, J., Akutsu, T., Cheung, Y. M., & Cai, H*. (2025). Unsupervised dual deep hashing with semantic-index and content-code for cross-modal retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(1): 387-399.
3. Zhang, B., Cai, H.*, Chen, J., Hu, Y., Huang, J., Rong, W., ... & Peng, H. (2022). Fast and accurate clustering of multiple modality data via feature matching. IEEE Transactions on Cybernetics, 52(6), 5040-5050.
4. Cai, H., Zhang, B.*, Li, J., Hu, B., & Chen, J. (2024). Unsupervised dual hashing coding (UDC) on semantic tagging and sample content for cross-modal retrieval. IEEE Transactions on Multimedia, 26, 9109-9120.
5. Zhang, Y., Quan, W., Akutsu, T., Liu, L., Cai, H., & Zhang, B.* (2024). Accurate multi-view clustering to seek the cross-viewed yet uniform sample assignment via tensor feature matching. Information Sciences, 664, 120305.
6. Zhang, B., & Cai, H.* (2021, December). Multi-omics data clustering via the guidance of highly correlated features. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (pp. 789-792). IEEE.
7. Wu, Q.#, Zhang, B. #, Hu, Y. #, Liu, B., Cao, D., Yang, D., ... & Yu, H.* (2021). Detection of morphologic patterns of diabetic macular edema using a deep learning approach based on optical coherence tomography images. Retina, 41(5), 1110-1117.
8. Liu, B. #, Zhang, B. #, Hu, Y. #, Cao, D., Yang, D., Wu, Q., ... & Yu, H.* (2021). Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning. Annals of Translational Medicine, 9(1), 43.
9. Zhang, B., Zhu, L., Sun, J., & Zhang, H.* (2018). Cross-media retrieval with collective deep semantic learning. Multimedia Tools and Applications, 77(17), 22247-22266.
10. Zhang, B., Zhang, H.*, Sun, J., Wang, Z., Wu, H., & Dong, X. (2018). Modality-Reconstructed Cross-Media Retrieval via Sparse Neural Networks Pre-Trained by Restricted Boltzmann Machines. Journal of Advanced Computational Intelligence and Intelligent Informatics, 22(5), 611-620.
11. Zhang, B., Zhang, H.*, Sun, J., Wang, Z., Wu, H., & Dong, X. (2017, August). Cross-media semantic matching via sparse neural network pre-trained by deep restricted Boltzmann machines. In International Conference on Internet Multimedia Computing and Service (pp. 280-289). Springer.
12. Wei, J., Zhang, B., Wang, Q., Zhou, T., Tian, T., & Chen, L.* (2025). Diffusive topology preserving manifold distances for single-cell data analysis. Proceedings of the National Academy of Sciences, 122(4), e2404860121.
13. Liu, L., Zhang, B., Zhang, H.*, & Zhang, N. (2019). Graph steered discriminative projections based on collaborative representation for Image recognition. Multimedia Tools and Applications, 78(17), 24501-24518.
14. Huang, J., Chen, J., Zhang, B., Zhu, L., & Cai, H.* (2021). Evaluation of gene–drug common module identification methods using pharmacogenomics data. Briefings in Bioinformatics, 22(3), bbaa087.
15. Zhang, Y., Huang, Q., Zhang, B., He, S., Dan, T., Peng, H., & Cai, H.* (2021). Deep multiview clustering via iteratively self-supervised universal and specific space learning. IEEE Transactions on Cybernetics, 52(11), 11734-11746.
16. Wang, H., Han, G., Zhang, B., Tao, G., & Cai, H.* (2021). Multi-view learning a decomposable affinity matrix via tensor self-representation on grassmann manifold. IEEE Transactions on Image Processing, 30, 8396-8409.
17. Wang, H., Han, G., Zhang, B., Tao, G., & Cai, H.* (2020). Exsavi: Excavating both sample-wise and view-wise relationships to boost multi-view subspace clustering. Neurocomputing, 418, 66-78.
18. Wang, H., Han, G., Zhang, B., Hu, Y., Peng, H., Han, C., & Cai, H.* (2020, December). Tensor-based low-rank and graph regularized representation learning for multi-view clustering. In IEEE International Conference on Bioinformatics and Biomedicine (pp. 821-826). IEEE.
19. Qi, Y., Zhang, H.*, Zhang, B., Wang, L., & Zheng, S. (2019). Cross-media retrieval based on linear discriminant analysis. Multimedia Tools and Applications, 78(17), 24249-24268.
20. Zheng, S., Zhang, H.*, Qi, Y., & Zhang, B. (2019). Modal-dependent retrieval based on mid-level semantic enhancement space. IEEE Access, 7, 49906-49917.
Patents:
1. An Image Retrieval Text Method Based on Sparse Neural Networks, Patent No. CN 106777402 B. Authors: Huaxiang Zhang, Bin Zhang, Zhenhua Wang, Qiang Wang, Lili Meng, Yuwei Ren, Hongchen Wu, Peilian Guo, Hui Ji, Xiuting Shao, Shengtao Li. Granted: 11 September 2018
2. A ROP Image Classification System and Method Based on Deep Learning, Patent No. CN202210661154.X. Authors: Honghua Yu, Xiaohong Yang, Qiaowei Wu, Yijun Hu, Bin Zhang, Ying Fang. Granted: 04 July 2023
3. A Method and Terminal for DME Classification Processing Based on Deep Neural Networks, Patent No. CN202010851829.8. Authors: Honghua Yu, Hongmin Cai, Qiaowei Wu, Bin Zhang, Baoyi Liu. Granted: 05 September 2023
4. Yu, H., Yang, X., Cai, H., Liu, B., Zhang, B., Huang, M., Wu, Q., and Fang, Y., DME Prognostic Information Prediction System Based on Integrated Machine Learning and Application Method, 2021, Australia, No. 2021100492.
Grants:
1. National Natural Science Foundation of China (NSFC), Major Research Plan, Decoding Immunity Big Data for the Onset, Progression and Precision Therapy of Systemic Lupus Erythematosus, Jan. 2025 – Dec. 2028, 3.0 million RMB. Sub-project PI. Responsible for the construction of immunity big data decoding platform, 0.8 million RMB.
2. National Natural Science Foundation of China (NSFC), Young Scientists Foundation (Category C), Research on Deep Fusion of Multi-Omics Data Based on Pre-trained Deep Hash Large Models, Jan. 2025 – Dec. 2027, 0.3 million RMB. Principal Investigator (PI).
3. China Postdoctoral Science Foundation, General Grant, Mining and Deep Fusion of Shared and Complementary Information in Multi-Omics Data, Jun. 2023 – Mar. 2025, 0.08 million RMB. Principal Investigator (PI).
4. National Natural Science Foundation of China (NSFC), General Program, High-order Information Modeling for Multi-source Biomedical Data, Jan. 2026 – Dec. 2029, 0.5 million RMB. Participant. Received funding of 0.1 million RMB.
5. National Natural Science Foundation of China (NSFC), Key Supported Project of Joint Fund, Intelligent Analysis of Multimodal Medical Images for Brain Diseases, Jan. 2022 – Dec. 2025, 2.6 million RMB. Participant.
6. National Natural Science Foundation of China (NSFC), General Program, Mechanism and Therapeutic Response Prediction of Diabetic Macular Edema Based on Association Networks of Retinal Images and Proteomic Features, Jan. 2022 – Dec. 2025, 0.59 million RMB. Participant.
7. National Natural Science Foundation of China (NSFC), General Program, Cross-Modal Data Retrieval Based on Local Semantic Association and Discriminant Analysis, Jan. 2018 – Dec. 2021, 0.66 million RMB. Participant.
8. South China University of Technology, Outstanding Young Scholar Cultivation Program (Fund for Central Universities), Computational Research on Drug Discovery and Correlation Analysis Based on Life Omics and Radiomics, Jan. 2019 – Dec. 2020, 0.3 million RMB. Participant.
