Lydia T. Liu
I am assistant professor of Computer Science at Princeton University. Currently, I am most interested in the scientific and normative foundations of machine learning and algorithmic decision-making, with a focus on societal impact and welfare outcomes. I am an faculty affliate of the Center for Information Technology Policy and Center for Statistics and Machine Learning.
I obtained my Ph.D. in Electrical Engineering and Computer Sciences from University of California, Berkeley, in May 2022, advised by Moritz Hardt and Michael I. Jordan. In 2022-2023, I was a postdoctoral associate at Cornell University Computer Science, working with Jon Kleinberg, Karen Levy, and Solon Barocas in the Artificial Intelligence, Policy, and Practice (AIPP) initiative.
I am the recepient of an Amazon Research Award, a Microsoft Ada Lovelace Fellowship, an Open Philanthropy AI Fellowship, an NUS Development Grant, and an ICML Best Paper Award.
For general audience articles about my recent work, see features by Center for Statistics and Machine Learning and Department News.
Speaker Bio
Lydia Liu is an Assistant Professor of Computer Science at Princeton University. Her research examines the theoretical foundations of machine learning and algorithmic decision-making, with a focus on long-term societal impact. She obtained her Ph.D. in electrical engineering and computer sciences from the University of California, Berkeley, and completed her postdoctoral research at Cornell University at the Artificial Intelligence, Policy, and Practice (AIPP) initiative. She is the recipient of an Amazon Research Award, fellowships from Microsoft and Open Philanthropy, and an ICML Best Paper Award.
Current PhD Students
Prospective Students and Postdocs
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Selected Publications
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Lydia T. Liu, Solon Barocas, Jon Kleinberg, Karen Levy.
On the Actionability of Outcome Prediction.
Proceedings of the AAAI conference on Artificial Intelligence, to appear (2024). [arxiv]
Research Summary featured by the Montreal AI Ethics Institute. -
Lydia T. Liu*, Serena Wang*, Tolani Britton^, Rediet Abebe^.
Reimagining the Machine Learning Life Cycle to Improve Educational Outcomes of Students.
Proceedings of the National Academy of Sciences 120.9 (2023): e2204781120. [arxiv] -
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt.
Delayed Impact of Fair Machine Learning.
Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. [arxiv]
News
Jan 2025
- I am serving as ethics chair of ICML 2025, alongside Kevin Jamieson. Grateful for the opportunity to give back to the ICML community!
- I will be an invited speaker at the Women in Theory Workshop at the Simons Institute, Berkeley, CA, Jun 3 – 6, 2025.
- I will be an invited speaker at the AAAI Workshop on Innovation and Responsibility in AI-Supported Education (iRAISE), Philadelphia, PA, March 3, 2025.
- Paper with Josh Cohen on The Reach of Fairness to appear in Journal for Responsible Computing, addressing fundamental and practical questions about the scope of algorithmic fairness.
- Paper with Inioluwa Deborah Raji on Designing Experimental Evaluations of Algorithmic Interventions with Human Decision Makers In Mind accepted in AISTATS 2025!
- Successfully wrapped up the first graduate course at Princeton on “AI, Society, and Education”, with guest speakers from Khan Academy, Duolingo, Stanford, and more. Congratulations to three course project groups for their acceptance to the AAAI 2025 iRAISE Workshop - including one spotlight presentation!
Aug 2024
- Excited to be recognized in the Princeton Engineering Commendation List for Outstanding Teaching for COS 598I.
Email: ltliu_at_princeton_dot_edu