陈灿贻，统计学博士，2018 年毕业于北京师范大学数学与应用数学专业，获理学学士学位；2023 年毕业于中国人民大学统计学专业，获统计学博士学位，后于美国密歇根大学安娜堡分校生物统计系从事博士后研究。主要研究方向包括分布式统计学习、因果中介分析和合成数据分析等。

Canyi Chen received his B.S. degree in Mathematics and Applied Mathematics from Beijing Normal University in 2018 and his Ph.D. degree in Statistics from Renmin University of China in 2023. Since then, he has worked as a postdoctoral researcher in the Department of Biostatistics at the University of Michigan, Ann Arbor. His research interests include distributed statistical learning, causal mediation analysis, and synthetic data analysis.

主要研究领域包括分布式统计学习、因果中介分析、合成数据分析、高维统计推断、联邦学习。

欢迎对统计学、机器学习、数据科学和生物统计等方向感兴趣的同学联系交流。

2024 年获中国人民大学优秀博士学位论文奖

国家自然科学基金面上项目（12171477），针对大数据的非线性相依关系度量与检验，2022.01–2025.12（参与）

[1]Chen, C., Qiao, N., and Zhu, L., 2025. Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine, Journal of the American Statistical Association.

[2]He, C., Chen, C., and Zhu, L., 2025. A Goodness-of-fit Assessment for General Learning Procedure in High Dimensions, Journal of the American Statistical Association.

[3]Chen, C., Zhu, Z., and Zhu, L., 2026. Efficient Decoding from Heterogeneous 1-Bit Compressive Measurements over Networks, Statistica Sinica.

[4]Qiao, N., Li, W., Zhang, J., and Chen, C.*, 2026. Scalable and Distributed Individualized Treatment Rules for Massive Datasets, Biometrics.

[5]Chen, B., and Chen, C.*, 2024. Convoluted Support Matrix Machine in High Dimensions, Statistica Sinica.

[6]Qiao, N., and Chen, C.*,2024. Fast and Robust Low-Rank Learning over Networks: A Decentralized Matrix Quantile Regression Approach, Journal of Computational and Graphical Statistics, 33(4), 1214–1223.

[7]Chen, C., Gu, Y., Zou, H., and Zhu, L., 2023. Distributed Sparse Composite Quantile Regression in Ultrahigh Dimensions, Statistica Sinica, 33, 1143–1167.

[8]Chen, C., and Zhu, L., 2022. Distributed Decoding from Heterogeneous 1-Bit Compressive Measurements, Journal of Computational and Graphical Statistics, 32(3), 884–894.