pc蛋蛋

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。

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.