I am a ML Research Scientist @  Apple AI/ML. Prior to that, I receieved my Ph.D. from the University of California, Davis in 2023, advised by Prof. Chen-Nee Chuah, Prof. Sen-Ching Cheung, and Prof. Brittany N. Dugger. I received my bachelor’s degree from Zhejiang University in 2019.

My research interests include multimodality (multimodal LLM), semi-supervised learng and label/data-efficient learning, AI healthcare.

In my free time, I like to play tennis, hiking and outdoor adventures. I also like to talk to people from various backgrounds and learn different life styles. Feel free to chat with me.

Recent News

• Two first-authored papers (VeCLIP and PathCLIP) are accepted by ECCV 2024.
• I’m honored to receive 2024 College of Engineering (COE) Excellence in Graduate Student Research Award and 2024 ECE Best PhD Disseration Award.
• One first-authored paper, “Semi-Path: An Interactive Semi-supervised Learning Framework for Gigapixel Pathology Image Analysis,” was accepted by Smart Health and will present at IEEE/ACM CHASE 2024.
• I receieve my PhD degree from UC Davis will join Apple AI/ML (Cupertino, CA) as a ML Researcher in Nov 2023.
• One first-authored paper, “PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation,” was accepted by ICCV 2023.
• I’m honored to receive Advancement-to-Candidacy (AC) fellowship from ECE at UC Davis.
• I will join Apple AIML (Cupertino, CA) as a ML Research Intern in March 2023.
• I’m honored to receive the Best Paper Award (Top 3) from Workshop on Learning with Limited Labelled Data for Image and Video Understanding at CVPR 2022.
• I’m honored to receive the ICML 2022 Participation Grant and R13 Grant Travel Awards from the American Association of Neuropathologists Annual (AANP).
• I will join Amazon Lab126 (Sunnyvale, CA) as an Applied Scientist Intern in June, 2022.
• Our work on ‘’Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution’’ was accepted by The International Conference on Machine Learning (ICML) 2022.