Ruiting Liang
Hi! I am a fifth year PhD student in Computational and Applied Mathematics at the University of Chicago, where I am very fortunate to be advised by Professor Rina Foygel Barber. Prior to this, I received my B.S. in Mathematics from the University of Chinese Academy of Sciences.
I work on the theory of statistical problems in machine learning, with the goal of understanding uncertainty in diverse settings under minimal and actionable assumptions. My research topics include distribution-free inference, algorithmic stability, and uncertainty quantification.
I also collaborate with astrophysicists on hypothesis testing problems arising in astronomical data analysis, with the support from the NSF–Simons AI Institute for the Sky (SkAI institute).
Papers
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False positive control in time series coincidence detection
Ruiting Liang, Samuel Dyson, Rina Foygel Barber, and Daniel E. Holz. arXiv:2512.17372 -
Assumption-free stability for ranking problems
Ruiting Liang, Jake A. Soloff, Rina Foygel Barber, and Rebecca Willett. arXiv:2506.02257 -
Conformal prediction after data-dependent model selection
Ruiting Liang, Wanrong Zhu, and Rina Foygel Barber. arXiv:2408.07066 -
Algorithmic stability implies training-conditional coverage for distribution-free prediction methods
Ruiting Liang and Rina Foygel Barber. Annals of Statistics 53(4):1457-1482. arXiv:2311.04295
Teaching
As a Teaching Assistant at the University of Chicago:
- STAT/CAAM 30900 Mathematical Computation I: Matrix Computation Course, Autumn 2022
- STAT/CAAM 31210 Applied Functional Analysis, Winter 2023-2025
- STAT/CAAM 31220 Partial Differential Equations, Spring 2023-2024
- STAT/CAAM 31430 Applied Linear Algebra, Autumn 2023
My teaching has been recognized with a 2023 CAM Outstanding TA Award.
Miscellaneous
Beyond research, a few personal notes can be found here.