Lydia T. Liu
Assistant Professor of Computer Science, Princeton University
Hi there! I am a computer scientist at Princeton University, where I direct the HAIKU Lab for human-AI collaboration. I am broadly interested in the scientific and normative foundations of machine learning and algorithmic decision-making, with a focus on societal impact and welfare. I am a faculty affiliate of the Center for Information Technology Policy, Center for Statistics and Machine Learning, Princeton Human Computer Interaction, Princeton Language and Intelligence, and the AI Lab.
I am the recipient of a Spencer Foundation Award, an Amazon Research Award, a Microsoft Ada Lovelace Fellowship, an Open Philanthropy AI Fellowship, an NUS Development Grant, and an ICML Best Paper Award.
I obtained my Ph.D. in Electrical Engineering and Computer Sciences from University of California, Berkeley, in 2022, advised by Moritz Hardt and Michael I. Jordan. 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.
General audience articles about my recent work: Rethinking AI’s impact on society through the lens of fairness by CSML, Department News.
My work as a poet has been supported by a MacDowell fellowship.
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.
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Selected Publications
Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising.
On the Actionability of Outcome Prediction.
Reimagining the Machine Learning Life Cycle to Improve Educational Outcomes of Students.
Delayed Impact of Fair Machine Learning.
News
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