Ye Tian (田野)

Portrait photo taken in front of Bailey Hall at Cornell with Yongxin Shang and friends during our engagement celebration.

Email: Uniform({ye.t@columbia.edu, ye.tian@yale.edu})
Address: 300 George St, Suite 503, New Haven, CT 06511

I am currently a Postdoc Researcher in Department of Biostatistics, Yale University, working with Prof. Hongyu Zhao in Zhao Lab. I got my PhD in Statistics from Columbia University and was honored to be advised by Prof. Yang Feng and Prof. Zhiliang Ying. I was born in Huainan, which is a small city of China famous for its long history and the delicious tofu there. Prior to Columbia, I obtained my Bachelor’s degree in Statistics in 2019 from School of the Gifted Young (SGY) at University of Science and Technology of China (USTC) and was honored to be supervised by Professor Weiping Zhang. I also spent a wonderful summer at Amazon as an applied scientist intern in 2023.

My 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, my 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.

Besides research, I love photography, running, traveling, and reading (especially Sci-Fi and history books). I also do some daily strength training. 

My Google Scholar page

News

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]

Honors

  • 2025/01: 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)

Service

Reviewer

  • Journal:
    • Annals of Statistics (3)
    • Annals of Applied Statistics (3)
    • Bernoulli (1)
    • Biometrics (3)
    • Biometrika (1)
    • Bioinformatics (2)
    • Computational Statistics and Data Analysis (2)
    • Dementia and Geriatric Cognitive Disorders (1)
    • Electronic Journal of Statistics (3)
    • Journal of Business and Economic Statistics (3)
    • Journal of Computational and Graphical Statistics (1)
    • Journal of Econometrics (3)
    • Journal of Machine Learning Research (5)
    • Journal of the American Statistical Association (13)
    • Journal of the Royal Statistical Society (Series B) (7)
    • Neural Networks (2)
    • Neurocomputing (1)
    • Scientific Reports (1)
    • Stat (4)
    • Statistics and Computing (3)
    • Statistics in Medicine (1)
    • Statistical Papers (2)
    • Statistica Sinica (5)
    • Technometrics (2)
  • Conference:
    • AISTATS 2024 (6)
    • FODS 2020 (3)
    • ICLR 2023 (1)

Conference session/Seminar organizers

  • 2024/08: Chair of the session “Applications in Statistical Learning and Data Science” at JSM 2024
  • 2023/08: Chair of the session “Statistical Learning Theory” at JSM 2023
  • 2021/08-2022/05: Columbia Statistics Student Seminar co-organizer (with Arnab Auddy)