Ye Tian

Assistant Professor
Headshot
Ye
Tian
He/Him/His
Assistant Professor
Department Affiliations
Interests
Transfer/Multi-task/Federated learning
Representation learning
Imbalanced classification
High-dimensional statistics
Address
Website
https://yet123.com

Biography

Ye Tian is currently an Assistant Professor of Statistics at Penn State University. Ye was a Postdoc Associate in Department of Biostatistics, Yale University, working with Prof. Hongyu Zhao in Zhao Lab, during 2025-2026. Ye got his PhD in Statistics from Columbia University in 2025 and was advised by Prof. Yang Feng and Prof. Zhiliang Ying. Prior to Columbia, Ye obtained his Bachelor’s degree in Statistics in 2019 from School of Gifted Young (SGY) at University of Science and Technology of China (USTC).

Ye's research lies at the intersection of statistics and machine learning, with the goal of developing more reliable machine learning (ML) systems and understanding their underlying mechanisms. Specifically, his research focuses on a few key areas: 
(1) Reliable transfer learning and related development in genetics; 
(2) Imbalanced classification error control for minority groups; 
(3) High-dimensional statistics and general ML.

Honors

  • 2025/01: ASA SLDS Student Paper Award
  • 2024/08: SIAM Student Travel Award
  • 2023/10: Howard Levene Outstanding Teaching Award (Department of Statistics, Columbia University)
  • 2023/06: NESS Student Research Award (The 36th New England Statistics Symposium, Boston)
  • 2023/04: IMS Hannan Graduate Student Travel Award
  • 2022/12: Student Paper Award (IMS International Conference on Statistics and Data Science, Florence, Italy)
  • 2022/11: Columbia ASGC Graduate Student Travel Award (The Arts and Sciences Graduate Council, Columbia University)
  • 2022/10: Columbia GSAS Student Conference Travel Award (Graduate School of Arts and Sciences, Columbia University)

Some representative works

  • Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness
    Tian, Y., Gu, Y., & Feng, Y. 
    Journal of Machine Learning Research (2025). [PDF]
  • Neyman-Pearson Multi-class Classification via Cost-sensitive Learning
    Tian, Y., & Feng, Y.
    Journal of the American Statistical Association (2025) [PDF]
  • Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms
    Tian, Y., Weng, H., & Feng, Y. 
    International Conference on Machine Learning (2024). [PDF]
  • Transfer Learning under High-dimensional Generalized Linear Models
    Tian, Y., & Feng, Y. (2023).
    Journal of the American Statistical Association (2023). [PDF]