I am an assistant professor in Computer Science and Engineering at The Chinese University of Hong Kong, heading the Scalable Principles for Learning and Reasoning Lab (SphereLab). I am also affiliated as a researcher with Max Planck Institute for Intelligent Systems. Previously, I finished my postdoc at Max Planck Institute for Intelligent Systems, working with Bernhard Schölkopf. I received a Ph.D. in Machine Learning from University of Cambridge, and a Ph.D. in Computer Science from Georgia Institute of Technology. I have also spent wonderful time at Google, Nvidia, and MERL.

I work primarily on principled modeling of inductive bias in learning algorithms. My research seeks to understand how inductive bias affects generalization, and to develop "light-yet-sweet" learning algorithms: (i) light: conceptually simple in methodology and easy to implement in practice, (ii) sweet: having clear intuitions and non-trivial theoretical guarantees.

Over the years, I always find myself fascinated by geometric invariance, symmetry, structures and how they can benefit generalization as guiding principles. Recently, I start rethinking inductive bias for foundation models, and develop a deep interest in large language models and generative modeling across visual, textual, and physical domains. More specifically, my current research focuses on (i) developing principled algorithms for training and adapting foundation models, and (ii) understanding how LLMs perform reasoning and eliciting it in verifiable scenarios (formal/math/symbolic reasoning).

I always believe in two principles in my research: (i) insight must precede application, and (ii) everything should be made as simple as possible, but not simpler. I try to follow certain research values.

    - Focus on creating novel ideas, not publishing papers
    - Follow curiosity and passion, not trends
    - Ideas are not owned, but come with debts to those who came before
    - Ideas become stronger when shared, discussed and criticized
    - Life is surprisingly short, so solve problems that interest and excite you most
    - It is good to be quick, but it is more important to be deep
    - Think like an amateur, do as an expert
    - This is not only about how to do research, but also how to live your life

I take great pleasure to work with a group of highly motivated students. Interested in joining? Make sure to read this first.

     - Thanks for your interest in joining us! See our group's recent focus.
     - I am always looking for motivated Postdoc, PhD students and visitors/interns. 
     - Due to the high volume of emails, I apologize if I haven't responded to your email.
     - Solid math/engineering and good communication skills are necessary.
     - NO need to email me. Fill out this form and directly apply here (and mention my name).

Ph.D. students

Visiting students

Alumni

     - Yamei Chen (2024): research intern
         - M.S. student at Technical University of Munich
         - Next: Ph.D. student in my group
     - Gege Gao (2023 - 2024): research intern
         - Ph.D. student at University of Tübingen
     - Zeju Qiu (2022 - 2024): master thesis student
         - M.S. at Technical University of Munich
         - Next: Ph.D. student in my group
     - Longhui Yu (2022 - 2024): research intern
         - M.S. at Peking University, Ph.D. offers from Caltech, University of Toronto
         - Next: Researcher at Kimi AI
     - Zhen Liu (2017 - 2019, 2022 - 2024): research intern
         - M.S. at Georgia Tech → Ph.D. at Mila & University of Montreal
         - Next: Assistant Professor at The Chinese University of Hong Kong, Shenzhen