I am a PhD student in the Machine Learning Department at Carnegie Mellon University, where I am advised by Steven Wu. My research centers around privacy-preserving machine learning. Previously, while completing my master's degree (also from CMU), I worked on multimodal machine learning and federated learning under Louis-Philippe Morency. Before that, I obtained my bachelor's degree in math and economics from the University of Chicago.
My work is supported by the Bloomberg Data Science Ph.D. Fellowship.
If you'd like to chat, please feel free to reach out at
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics [link][arXiv][code]
Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu (alphabetical order)
Proceedings of the National Academy of Sciences (PNAS)
TPDP 2023 (oral)
Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data [arXiv]
Paul Pu Liang*, Terrance Liu*, Anna Cai, Michal Muszynski, Ryo Ishii, Nicholas Allen, Randy Auerbach, David Brent, Ruslan Salakhutdinov, Louis-Philippe Morency
ACL 2021 (oral)
Think Locally, Act Globally: Federated Learning with Local and Global Representations [arXiv][code]
Paul Pu Liang*, Terrance Liu*, Liu Ziyin, Nicholas Allen, Randy Auerbach, David Brent, Ruslan Salakhutdinov, Louis-Philippe Morency
NeurIPS'19 Workshop on Federated Learning
(oral, distinguished student paper award)