Context-Enhanced Learning accepted by DATA-FM and DeLTa workshop in ICLR 2025
Come and chat with us @ Singapore!
About Me
I am a second year PhD student in Computer Science at Princeton University working on theoretical machine learning and language modeling. I am fortunate to be advised by Professor Sanjeev Arora. I did my undergrad at Duke, where I was fortunate to be advised by Professor Rong Ge.
Interests
- Machine Learning Theory
- Language Modeling
- Theoretical Computer Science
Education
Ph.D. in Computer Science
Princeton University
B.S. in Math & Computer Science
Duke University
Research Interests
My interest spans across both theoretical and empirical ML. Recently I am especially interested in understanding the power of large language models through a semi-theoretical lens. I have also worked on optimization dynamics of deep neural nets. Besides, I am also broadly interested in theoretical computer science and algorithmic fairness.
Recent News
New Paper - On the Power of Context-Enhanced Learning in LLMs
Check out our new paper on context-enhanced learning! When training LLMs, putting helpful information purely in-context can exponentially improve sample efficiency while leading to little verbatim memorization of the in-context materials!
Featured Publications
Recent Publications
(2025).
On the Power of Context-Enhanced Learning in LLMs.
arXiv.
(2023).
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example.
ICLR 2023.
(2023).
Fairness in the Assignment Problem with Uncertain Priorities.
AAMAS 2023.
Talks
