About me

I am currently a MEXT-funded Ph.D. candidate at The University of Tokyo, working with Toshihiko Yamasaki. Prior to that, I received a Master’s degree from Peking University in 2021, advised by Chao Zhang. Throughout my academic journey, I have had two wonderful internships at Adobe and SenseTime, and have been fortunate to have the opportunity to collaborate with Yuhui Yuan, Hongyang Zhang, and Shan You.

My research focuses on self-improving machine learning, primarily for vision data. I approach this challenge through two key aspects: learning algorithms and model design.

• On the algorithmic front, I develop methods to uncover and leverage the hidden self-supervision inherent within data, e.g., SAT, LEWEL, and SimMatch.
• On the model front, I design ‘fast learners’, foundational models that operate efficiently concerning both human- and self-supervisions, e.g., ISA, OCNet, and GreenMIM.

I am open to collaboration at any level on relevant topics. Feel free to shoot me an email.

News

• 2024/08: I gave an invited talk at MIRU 2024.
• 2024/07: One paper was accpted by ECCV 2024.
• 2023/07: One paper SimMatchv2 was accepted by ICCV 2023.
• 2023/05: I started an internship at Adobe, San Jose.
• 2023/03: Gave a talk on SAT (slides) at Waves in AI seminar.
• 2022/10: One paper (SAT) was accepted at T-PAMI.
• 2022/09: One paper (GreenMIM) was accepted at NeurIPS 2022.
• 2022/03: Two papers (LEWEL and SimMatch) were accepted at CVPR 2022.