Terrance Liu

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.

If you'd like to chat, please feel free to reach out at terrancl(at)andrew(dot)cmu(dot)edu.

* denotes equal contribution

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)

Policy Impacts of Statistical Uncertainty and Privacy [link][code]
Ryan Steed, Terrance Liu, Zhiwei Steven Wu, Alessandro Acquisti
Science (also presented at TPDP 2022)

Private Synthetic Data with Hierarchical Structure [arXiv]
Terrance Liu, Zhiwei Steven Wu
TPDP 2022

Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods [arXiv][code]
Terrance Liu*, Giuseppi Vietri*, Zhiwei Steven Wu
NeurIPS 2021

Leveraging Public Data for Practical Private Query Release [arXiv][code]
Terrance Liu, Giuseppi Vietri, Thomas Steinke, Jonathan Ullman, Zhiwei Steven Wu
ICML 2021

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)