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  • Resume
  • Research Fields
  • Enrollment and Training
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  • Scientific
  • Academic Achievements
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Rui Qiu

  • About

    • Department: School of Statistics
    • Gender: male
    • Post: Young Research Fellow
    • Graduate School: East China Normal University
    • Degree: Ph.D.
    • Academic Credentials:
    • Tel:
    • Email: rqiu@sfs.ecnu.edu.cn
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    WorkExperience

    2024.07–2026.06, Postdoctoral Researcher, Peking University (Postdoctoral Advisor: Professor Fang Yao)

    2026.07–Present, East China Normal University, Young Research Fellow

    Education

    2024, Ph.D., East China Normal University (Supervisor: Professor Zhou Yu)

    2023, Joint Ph.D. Program, National University of Singapore (Co-supervisor: Professor Zhenhua Lin)


    Resume

    Rui Qiu is a Youth Researcher and doctoral supervisor at the School of Statistics, East China Normal University. He was selected for the National Postdoctoral Program for Innovative Talents. His main research interests include statistical machine learning, non-Euclidean data analysis, sufficient dimension reduction, and deep learning. He is the principal investigator of a Young Scientists Fund project of the National Natural Science Foundation of China and has published multiple papers in leading journals and conferences, including The Annals of Statistics, JRSSB, JMLR, IEEE Transactions on Information Theory, and ICML.


    Other Appointments

    Research Fields

    Statistical Machine Learning, Non-Euclidean Data Analysis, Sufficient Dimension Reduction, Deep Learning

    Enrollment and Training

    Course

    Scientific

    1. Young Scientists Fund (Category C), National Natural Science Foundation of China, 2026–2028, Principal Investigator

    2. National Postdoctoral Program for Innovative Talents (Category A), 2024–2026, Principal Investigator

    3. General Program of the China Postdoctoral Science Foundation, 2024–2026, Principal Investigator


    Academic Achievements

    Selected Research Achievements:


    [5] Rui Qiu, Fang Yao, and Zhou Yu (2026). Fréchet regression with Mondrian forests: Finite‑sample

    guarantees and ensemble benefits. IEEE Transactions on Information Theory.


    [4] Yinfeng Chen, Jin Liu and Rui Qiu∗ (2025). Deep principal support vector machines for nonlinear

    sufficient dimension reduction. International Conference on Machine Learning.


    [3] Rui Qiu, Shuntuo Xu, and Zhou Yu (2024). Deep neural networks meet random forests. Journal

    of the Royal Statistical Society Series B: Statistical Methodology, 2024, 86(5): 1435‑1454.


    [2] Yinfeng Chen, Yuling Jiao, Rui Qiuand Zhou Yu (2024). Deep nonlinear sufficient dimension

    reduction. Annals of Statistics, 2024, 52(3): 1201‑1226.


    [1] Rui Qiu, Zhou Yu, and Ruoqing Zhu (2024). Random Forest Weighted Local Fréchet Regression

    with Random objects. Journal of Machine Learning Research, 2024, 25(107): 1‑69.













    Honor

    1. National Postdoctoral Program for Innovative Talents

    2. Boya Postdoctoral Fellowship, Peking University

    3. Outstanding Doctoral Dissertation, East China Normal University