About Me

Hi! I’m a Ph.D. student in EECS at UC Berkeley, where I’m fortunate to be advised by Prof. Yi Ma. I’m affiliated with BAIR and supported by a UC Berkeley College of Engineering fellowship. Prior to my PhD, I completed a BA in CS and MS in EECS, also at UC Berkeley.

My research interests broadly lie in simplifying deep learning. More specifically, I’m interested in developing theory to understand, improve, and simplify empirical deep learning methodology. I work on this problem through the following research threads:

  • understanding the latent representations learned by modern deep neural networks;
  • connecting modern deep learning practice to classical signal processing and statistics;
  • and leveraging such-obtained conceptual insights to design interpretable, efficient, and principled learning algorithms.

I’m particularly interested in problem instances where data is high-dimensional yet has rich structure, such as computer vision, natural language processing, and multi-modal contexts.

In my free time, I play basketball, chess, and TFT, and read sci-fi novels.

Notes for undergraduate and masters students.
Note 1: I'm happy to chat about my research or general advising. Please send me an email and we can work out a time.

Note 2: If you are interested in research collaboration, please send me an email with your background and specific interests (the more detailed, the better). The recommended time investment is at least 15 hours per week. Unfortunately, right now my schedule is tight and generally does not permit consistent long-term mentoring of younger students, so some degree of self-sufficiency would be highly valued. To ensure a more fruitful collaboration, it would be best to have the technical knowledge to read and understand deep learning papers, especially theory-oriented work. Thank you for your understanding.


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