Peihan Liu

Peihan Liu

CS PhD student, Columbia · peihanliu@cs.columbia.edu · Scholar

I'm a second-year CS PhD student in the Columbia theory group, advised by Rachel Cummings and Roxana Geambasu. I work on trustworthy machine learning — particularly the theoretical and empirical foundations of privacy, fairness, and modern machine learning.

Previously, I received my M.Eng. in CSE from Harvard, where I worked on algorithmic fairness with Cynthia Dwork and Juan Perdomo. Before that, I earned B.S. degrees in Mathematics and Statistics, with high honors and high distinction, from the University of Michigan, where I worked with Martin Strauss, Ranjan Pal, Shizhang Li, and Nuh Aydin on algorithmic fairness, homological algebra, and algebraic coding theory.

Beyond research, I walk my dog and (used to) play poker.

Experience

Publications

See Google Scholar for the up-to-date list.

  1. ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?
    Peihan Liu, Lucas Rosenblatt, Weiwei Kong, Natalia Ponomareva, Gautam Kamath, Rachel Cummings, Roxana Geambasu, Yu Gan, Lillian Tsai, Alex Bie
    preprint, 2026. [PDF] [Blog] [arXiv] [code] [dataset]
  2. Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data
    Lucas Rosenblatt, Peihan Liu, Ryan McKenna, Natalia Ponomareva
    ICML, 2026. [arXiv]
  3. Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space
    Sheng Yang, Peihan Liu, Cengiz Pehlevan
    TMLR, 2024. [arXiv]
  4. Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
    Alyssa Huang, Peihan Liu, Ryumei Nakada, Linjun Zhang, Wanrong Zhang
    preprint, 2024. [arXiv]

Blog