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 did my postdoc at Max Planck Institute for Intelligent Systems with Bernhard Schölkopf. I have received a Ph.D. in Machine Learning from University of Cambridge, and a Ph.D. in Computer Science from Georgia Tech. My advisors were Adrian Weller, Bernhard Schölkopf and Le Song. 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. My current research focuses on

• developing principled algorithms for training and adapting foundation models, as in OPT, OFT(v1,v2), BOFT, POET, VML;
• understanding how large language models perform reasoning and enchancing it in verifiable scenarios (formal/math/symbolic reasoning), as in MetaMath, SGP-Bench, BesiegeField.

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).

PhD 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