团队负责人：周勇
研究目标：
面向国家战略和急需，长期致力于大数据分析的基础理论、方法与技术的研究，建立大数据统计学习基础理论与方法，高性能的分布式并行计算算法，并把发展的大数据与复杂数据的分析技术与方法应用到数字经济、区块链和金融科技中，解决这些领域的重大基础问题。
代表性论文：
[1] Song, Shanshan; Lin, Yuanyuan; Zhou, Yong. Semi-supervised inference for block-wise missing data without imputation. Journal of Machine Learning Research. 2024.25:1-36.
[2] Song, Shanshan; Lin, Yuanyuan; Zhou,Yong. A General M-estimaion Theory in Semi-Supervised Framework. Joumal of the American Statisical Assocation. 2024, 119 (548).1065-1075.
[3] He, Yifan; Wu, Ruiyang; Zhou,Yong; Feng,Yang. DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation. Journal of the American StatisticalAssociation. 2024.119( 547):1933-1944.
[4] Chen, Lanjue; Wan, Alan T. K; Zhang, Shuyi; Zhou, Yong. Distributed Algorithms for U-statistics-based Empirical Risk Minimization. Journal of Machine Learning Research. 2023.24 :263.
[5] 周勇,张澍一,李子洋. 大数据下分位数回归通讯有效算法及其应用[J].管理科学学报, 2023,26(05):70-102.