I recently got my Ph.D. in Computer Science at Carnegie Mellon University, advised by Virginia Smith. Prior to CMU, I received undergraduate degrees in Computer Science and Economics from Peking University.
My research centers around optimization, trustworthy machine learning, and learning in heterogeneous environments, with applications to federated learning. I am interested in designing, analyzing, and evaluating principled learning algorithms, taking into account real-world constraints (e.g., communication and heterogeneity) to address issues related to accuracy, scalability, trustworthiness (fairness, robustness, and privacy), and their interplays.
I am currently working as a postdoctoral researcher at the Fundamental AI Research (FAIR) team at Meta. Starting in Summer 2024, I will join the University of Chicago as an Assistant Professor in the Department of Computer Science and the Data Science Institute. If you are interested in working with me, feel free to send me an email!