I am currently a Postdoctoral Fellow at the Department of Data Science, City University of Hong Kong (CityU). Prior to this, I was a Postdoctoral Fellow at The Chinese University of Hong Kong (CUHK). I obtained my Ph.D. from Nanjing University of Aeronautics and Astronautics (NUAA). My research focuses on principled and scalable modeling of inductive bias for representation learning. In particular, I study how invariance, symmetry, and hierarchy serve as inductive biases for learning, and develop frameworks that are theoretically grounded, structurally transparent, and compatible with scaling. My research has been published in top-tier journals and conferences, such as ACM Computing Surveys, IEEE TPAMI, IEEE TIP, ICCV, and USENIX Security.