Published multiple papers, including those accepted by NeurIPS-2025, ICCV-2025, TMI-2025, ICLR-2025, and other top-tier conferences and journals. Specific publications include:
- ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decoding
- Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstruction
- S&D Messenger: Exchanging Semantic and Domain Knowledge for Generic Semi-Supervised Medical Image Segmentation
- Other research papers on Foundation Models, Label-efficient Learning, and Medical Image Segmentation.
Research Experience
Conducting Ph.D. research in Prof. Xiaomeng Li's lab, focusing on Computer Vision and Brain Decoding.
Education
Received a Master's degree from The University of Hong Kong and a B.Eng degree from Northeastern University, supervised by Prof. Peng Cao. Currently pursuing a Ph.D. at The Hong Kong University of Science and Technology, supervised by Prof. Xiaomeng Li.
Background
Ph.D. student at The Hong Kong University of Science and Technology, focusing on Computer Vision. Research interests include Deep Learning, Computer Vision, Label-efficient Learning, Medical Image Analysis, Foundation Models, Brain Decoding, and Generative Models.
Miscellany
Personal interests and other information not provided.