Dr. Yuexiang Li

Associate professor, master supervisor

Email：yxli@cityu.edu.mo

Tel： (853) 85902484

Office address:  Room S401, Stanley Ho Building, City University of Macau (Taipa)

Educational qualifications 

2016 PhD in Electronic Engineering, University of Nottingham, United Kingdom

2012 Master of Science in Electronic Information Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

2011 Bachelor of Engineering in Telecommunications Engineering and Management, Beijing University of Posts and Telecommunications, China

Scientific research experience 

Senior Researcher, Tencent Jarvis Lab

Postdoctoral Researcher, Shenzhen University

Incumbent 

Associate Professor, Faculty of Data Science, City University of Macau

Research direction 

Foundational AI Models: This research direction aims to transcend the limitations of single-modal large models (e.g., pure language or vision), focusing on advancing the core technologies of multimodal large models (e.g., Large Vision-Language Models). The key exploration areas include their capabilities in scene understanding, logical reasoning, and autonomous task decomposition within complex open-world environments. It is also dedicated to addressing critical bottlenecks related to model trustworthiness, safety, and efficiency. The research will be closely integrated with industrial needs to drive its deep application and value realization in key fields such as intelligent content generation, scientific computing, embodied AI, and human-AI collaboration.

Computer Vision: This direction moves beyond traditional paradigms like image classification and recognition, adopting a "creation and editing"-driven approach to advance computer vision technology. Research will center on cutting-edge architectures like Diffusion Models and Transformers, focusing on enhancing models' capabilities for fine-grained control, semantic consistency, and creativity in cross-modal "text-image-video" generation tasks. Simultaneously, it will explore how to deeply integrate world knowledge and commonsense reasoning into the generation process, achieving a leap from passive perception to active creation, and from pixel synthesis to semantic generation, thereby providing a core engine for the AIGC industry.

Medical Image Analysis: This direction focuses on solving the "last-mile" challenge of translating AI medical imaging technology from the lab to clinical application. The core objective extends beyond improving the accuracy of lesion detection and segmentation to building interpretable, robust, and clinician workflow-adaptive decision support systems. Research will prioritize exploring techniques such as few-shot learning, domain adaptation, and uncertainty quantification to tackle challenges posed by scarce clinical data and distribution shifts. Through rigorous prospective clinical trials and multi-center validation, it will scientifically assess the practical value of these systems for clinical diagnostic pathways, prognostic evaluation, and treatment planning, ultimately facilitating their transformation into reliable clinical tools.

Master's and Doctoral candidates are being recruited for all research directions. Inquiries are welcome via email.

Research and publishing

Selected Journal Papers：

[1] J. Qiu, J. Cao, Y. Huang, Z. Zhu, F. Wang, C. Lu, Y. Li* & Y. Zheng (2025). MUSCLE: A new perspective to multi-scale fusion for medical image classification based on the theory of evidence. IEEE Transactions on Medical Imaging, Early Access. (IF=9.8)

[2] W. Zhang, H. Liu, J. Xie, Y. Huang, Y. Zhang, Y. Li*, R. Ramachandra & Y. Zheng (2024). Anomaly detection via gating highway connection for retinal fundus images. Pattern Recognition, 148, 110167. (IF=7.6)

[3] Y. Yan, H. Wang, Y. Huang, N. He, L. Zhu, Y. Xu, Y. Li* & Y. Zheng (2024). Cross-modal vertical federated learning for MRI reconstruction. IEEE Journal of Biomedical and Health Informatics, 28(11), 6384-6394. (IF=6.8)

[4] Y. Li*, Y. Wang, G. Lin, Y. Huang, J. Liu, Y. Lin et al. (2024). Triplet-branch network with contrastive prior-knowledge embedding for disease grading. Artificial Intelligence in Medicine, 149, 102801. (IF=6.2)

[5] J. Zhu, Y. Li*, Y. Hu, K. Ma, S. K. Zhou & Y. Zheng (2020). Rubik’s cube+: A self-supervised feature learning framework for 3D medical image analysis. Medical Image Analysis, 64, 101746. (IF=11.8, 200+ citations)

Selected Conference Papers：

[1] Q. Bi, J. Yi, H. Zheng*, H. Zhan, W. Ji, Y. Huang & Y. Li* (2025). Dgfamba: Learning flow factorized state space for visual domain generalization. The AAAI Conference on Artificial Intelligence (AAAI), 39(2), 1862-1870. (CCF-A類會議)

[2] Q. Bi, J. Yi, H. Zheng*, W. Ji, H. Zhan, Y. Huang, Y. Li* & Y. Zheng* (2024). Samba: Severity-aware recurrent modeling for cross-domain medical image grading. Advances in Neural Information Processing Systems (NeurIPS), 37, 75829-75852. (CCF-A類會議)

[3] Q. Bi, J. Yi, H. Zheng*, H. Zhan, Y. Huang, W. Ji, Y. Li* & Y. Zheng* (2024). Learning frequency-adapted vision foundation model for domain generalized semantic segmentation. Advances in Neural Information Processing Systems (NeurIPS), 37, 94047-94072. (CCF-A類會議)

[4] Q. Bi, J. Yi, H. Zheng*, W. Ji, Y. Huang, Y. Li* & Y. Zheng (2024). Learning generalized medical image segmentation from decoupled feature queries. The AAAI Conference on Artificial Intelligence (AAAI), 38(2), 810-818. (CCF-A類會議)

[5] H. Liu, W. Zhang, B. Li*, H. Wu, N. He, Y. Huang, Y. Li*, B. Ghanem & Y. Zheng (2023). Learning generalized medical image segmentation from decoupled feature queries. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16219-16229. (CCF-A類會議)

Please refer to my Google Scholar profile for a complete list of publications.

Academic Awards

World’s Top 2% Scientists，2022-2025

Professional association

2026 The AAAI Conference on Artificial Intelligence (AAAI)，CCF-A，Area Chair

2025 Medical Imaging Computing Seminar（MICS），Program Committee

2023-2024 The International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI)，CCF-B，Area Chair