Hongming Shan-Institute of Science and Technology for Brain-inspired Intelligence

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Hongming Shan
Research Direction：Machine Learning, Computer Vision, Medical Imaging and Analysis
Email：hmshan@fudan.edu.cn
Website：http://hmshan.io/

Brief Introduction：Dr. Hongming Shan is a Research Professor/Principal Investigator at the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University. Before joining Fudan University, he worked with Prof. Ge Wang at the Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute. He received his Ph.D. in machine learning from Fudan University under the supervision of Prof. Junping Zhang. He has published research papers in top journals including Nature Machine Intelligence、Nature Communications、IEEE TPAMI、IEEE SPM、IEEE TNNLS、IEEE TIP、IEEE TMI、MedIA, and in leading conferences such as ICCV、CVPR、NeurIPS、IJCAI. Dr. Shan was recognized with Youth Outstanding Paper Award at World Artificial Intelligence Conference in 2021.
Achievement：

Selected journal papers:
1. H. Shan, A. Padole, F. Homayounieh, U. Kruger, R. D. Khera, C. Nitiwarangkul, M. K. Kalra and G. Wang. Competitive Performance of a Modularized Deep Neural Network Compared to Commercial Algorithms for Low-Dose CT Image Reconstruction. Nature Machine Intelligence, 1(6), 269–276, 2019
2. Z. Huang, J. Chen, J. Zhang and H. Shan*. Learning Representation for Clustering via Prototype Scattering and Positive Sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), 7509-7524, 2023
3. Z. Huang, J. Zhang and H. Shan*. When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), 7917-7932, 2023
4. Q. Gao, Z. Li, J. Zhang, Y. Zhang and H. Shan*. CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization. IEEE Transactions on Medical Imaging, 43(2), 745-759, 2024
5. C. Wang, S. Piao, Z. Huang, Q. Gao, J. Zhang, Y. Li* and H. Shan*. Joint Learning Framework of Cross-modal Synthesis and Diagnosis for Alzheimer's Disease by Mining Underlying Shared Modality Information. Medical Image Analysis, 91(2024), 103032, 2024

Selected conference papers：
6. Y. Lei, Z. Li, Y. Li, J. Zhang and H. Shan*. LICO: Explainable Models with Language-Image Consistency. In Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, Dec. 10-16, 2023
7. Z. Huang, J. Zhang and H. Shan*. Twin Contrastive Learning with Noisy Labels. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, Jun. 18-22, 2023
8. Y. Wei, S. Zhang, Z. Qing, H. Yuan, Z. Liu, Y. Liu, Y. Zhang, J. Zhou and H. Shan*. DreamVideo: Composing Your Dream Videos with Customized Subject and Motion. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle WA, USA, Jun. 17-21, 2024
9. Z. Li, C. Ma, J. Chen, J. Zhang and H. Shan*. Learning to Distill Global Representation for Sparse-View CT. In Proceedings of International Conference on Computer Vision (ICCV), Paris, France, Oct. 2-6, 2023
10. C. Wang, T. Chen, Z. Chen, Z. Huang, T. Jiang, Q. Wang and H. Shan*. FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Jeju Island, South Korea, Aug. 3-9, 2024