I am assistant professor of Computer Science at Princeton University. My current research examines the theoretical foundations of machine learning and algorithmic decision-making, with a focus on societal impact and welfare.
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 received a Microsoft Ada Lovelace Fellowship, an Open Philanthropy AI Fellowship, an NUS Development Grant, and an ICML Best Paper Award.
Previously, I completed my B.S.E. in Operations Research and Financial Engineering at Princeton University. I have also spent two wonderful summers at Microsoft Research, most recently at MSR New England in 2019.
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]
Upcoming events
I am teaching a new graduate seminar on Responsible AI in Spring 2024!
I’m co-organizing the AAAI 2024 Workshop on AI for Education: Bridging Innovation and Responsibility, Feb 26-27, 2024, in Vancouver, BC, Canada!
Email: lydiatliu_at_berkeley_dot_edu