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Resume (English only)
Academic Achievements
He has received several awards, including MIT Technology Review Innovators Under 35 Asia Pacific (2025), IEEE AI’s 10 to Watch (2024), Schmidt Sciences AI2050 Early Career Fellow (2024), Alfred P. Sloan Research Fellowship (2024), Intel Rising Star Faculty Award (2023), Samsung AI Researcher of the Year (2022), National Science Foundation CAREER Award (2022), AAAI New Faculty Highlights (2021), CMU School of Computer Science Distinguished Dissertation Award Honorable Mention (2019), and Nvidia Pioneer Award (2018). He has published multiple significant papers on topics such as global convergence of gradient EM and policy-based trajectory clustering in offline reinforcement learning.
Research Experience
Prior to starting as faculty, he was a postdoc at the Institute for Advanced Study of Princeton. During his Ph.D., he worked at the Simons Institute and research labs of Facebook, Google, and Microsoft.
Education
He completed his Ph.D. in Machine Learning at Carnegie Mellon University, where he was co-advised by Aarti Singh and Barnabás Póczos. He was a postdoc at the Institute for Advanced Study of Princeton, hosted by Sanjeev Arora. Previously, he studied EECS and EMS at UC Berkeley. During his Ph.D., he also spent time at the Simons Institute and research labs of Facebook, Google, and Microsoft.
Background
He is an associate professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. His research interests are broadly in machine learning, such as foundation models, reinforcement learning, non-convex optimization, and data selection.
Miscellany
Prospective students, postdocs, and visitors are encouraged to send him an email with their CV.