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 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.
Selected Publications
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 (Spring 2024)
I received an Amazon Research Award for the proposal From Predictions to Positive Impact: Foundations of Responsible AI in Social Systems.
I am co-organizing the BIRS workshop on Bridging Prediction and Intervention Problems in Social Systems with Inioluwa Deborah Raji, Angela Zhou, and Arvind Narayanan, June 3-7, 2024. Talks will be available online.
Email: lydiatliu_at_berkeley_dot_edu