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.

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 terrancl(at)andrew(dot)cmu(dot)edu.

Publications
* denotes equal contribution

Generating Private Synthetic Data with Genetic Algorithms [arXiv][code]
Terrance Liu*, Jingwu Tang*, Giuseppe Vietri*, Zhiwei Steven Wu (alphabetical order)
ICML 2023

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)

Policy Impacts of Statistical Uncertainty and Privacy [link][code]
Ryan Steed, Terrance Liu, Zhiwei Steven Wu, Alessandro Acquisti
Science; 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)