Lydia T. Liu

Assistant Professor of Computer Science, Princeton University

lydia-liu.jpg

Photo credit: David Kelly Crow

Email
ltliu_at_princeton_dot_edu

Office
Department of Computer Science
35 Olden Street

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.

Prospective Students and Postdocs, please read this page before reaching out. Thank you!

Selected Publications

Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising.

Kara Schechtman, Benjamin Brandon, Jenise Stafford, Hannah Li^, Lydia T. Liu^. EAAMO, to appear, 2025.

On the Actionability of Outcome Prediction.

Lydia T. Liu, Solon Barocas, Jon Kleinberg, Karen Levy. AAAI, 2024.

Reimagining the Machine Learning Life Cycle to Improve Educational Outcomes of Students.

Lydia T. Liu*, Serena Wang*, Tolani Britton^, Rediet Abebe^. PNAS, 2023.

Delayed Impact of Fair Machine Learning.

Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt. ICML, 2018. Best Paper Award.

News

May 2026
  • I will be presenting a tutorial on “Bridging Prediction and Intervention Problems in Social Systems” at ACM FAccT 2026 in Montréal, Canada. Come say hi!
  • I am starting a new COS independent work seminar on Rethinking Human-AI Collaboration through Design for Fall 2026. In this seminar, students will design and build original human-AI collaboration tools that center human knowing.
Mar 2026
  • We received a Spencer Foundation award to study AI-supported advising and its implications for equity in education!
  • We’re excited to welcome Eve Fleisig as a Postdoctoral Fellow at Princeton CITP, starting in Fall 2026, co-advised by Arvind Narayanan and me!
  • I gave an invited talk on Expertise in Age of Human-AI Collaboration at the NYU C+M Center AI 2026 Conference, The Human Future in the Age of AI, and shared on my thoughts on “AI and the Future of Work”.
  • I am reprising my role as ethics co-chair of ICML 2026, alongside Asia Biega!
Oct 2025
  • Paper with Kara Schechtman, Benjamin Brandon, Jenise Stafford, and Hannah Li on Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising accepted for oral presentations at ACM EAAMO 2025 and CODE 2025.
  • Paper with Amaya Dharmasiri, William Yang, Polina Kirichenko, and Olga Russakovsky on The Impact of Coreset Selection on Spurious Correlations and Group Robustness accepted at NeurIPS 2025.
  • Excited to be invited as a Kavli Fellow to present work at the 2025 Japanese-American-German Kavli Frontiers of Science Symposium in Irvine, CA. Poster of Recent Work
Apr 2025
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.