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. Broadly, I am passionate about understanding global changes and contributing technology to the world.
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.
I had the privilege to conduct multiple internships. 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. In 2022 fall I am a part-time researcher at Google Brain. In my spare time I enjoy staying active, film photography, playing piano, listening to music. My pronouns are she/her/hers.
- Our work Off-Policy Evaluation with Policy-Dependent Optimization Response will be presented at NeurIPS 2022. Check it out!
- Our work Multi-Source Causal Inference Using Control Variates is accepted to TMLR. Check it out!
- I am excited to attend the 2022 Rising Stars in Machine Learning, hosted by University of Maryland.
- I am excited to give a talk at the Workshop in Operations Research and Data Science (WORDS'22) at Duke University.
- I am excited to attend the 2022 EECS Rising Stars Workshop, hosted at the University of Texas, Austin.