Hi! I am a PhD candidate in Computer Science at University of California, Berkeley, where I am very fortunate to be advised by Michael I. Jordan. Prior to that I received a B.Sc. triple-majored in Physics, Mathematics and Computer Science from Hong Kong University of Science and Technology (with first class honors).
My research interests lie at the intersection of machine learning, statistics and economics, in particular building machine learning systems that are more robust, equitable, and responsive to the evolving social and economic contexts. Often these contexts lead to challenges on noisy data, distribution shifts, strategic responses, multi-agent dynamics.
I am affiliated with Berkeley AI Research (BAIR), the CLIMB Center, and RISE Lab. I am generously supported by a Google Ph.D. Fellowship in Algorithms, Optimizations and Markets and a Berkeley Graduate Fellowship.
In 2021 summer, I was a research intern at Microsoft Research New England, hosted by David Alvarez-Melis. In 2022 spring I was a part-time machine learning intern at CNN. Starting 2022 fall I am a part-time researcher at Google Brain. In my spare time I like long-distance running, film photography, playing piano, listening to music. My pronouns are she/her/hers.
- Our work Off-Policy Evaluation with Policy-Dependent Optimization Response is accepted at NeurIPS 2022. Check it out!
- I am excited to join Google Brain as a part-time researcher.
- I am delighted to give a talk at Workshop in Operations Research and Data Science (WORDS'22) at Duke University.
- I am delighted to be invited to the 2022 EECS Rising Stars Workshop. I will give a talk at the workshop at the University of Texas, Austin.
- Our work No-Regret Learning in Partially-Informed Auctions is presented at ICML 2022. Check it out!