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 and Prof. Jiantao Jiao. 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 and internal algorithms learned by modern deep neural networks, usually from the perspectives of signal processing, statistics, and information theory;
  • leveraging such-obtained conceptual insights to design interpretable, efficient, and principled improvements and alternatives to existing deep learning practices and methods.

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

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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