基本信息

                 徐   晨

           教授、国家级人才
办公室：数学楼207(数学与统计学院)
      电子邮件：cx3@xjtu.edu.cn

研究领域（方向）

大数据统计学，高维统计，统计学习理论及算法

个人及工作简历

西安交通大学领军学者及兼职讲座教授、鹏城国家实验室研究员、国家重大人才计划入选者、深圳孔雀计划特聘专家。2012年于加拿大不列颠哥伦比亚大学取得统计学博士学位，而后赴美国宾州州立大学（2013-2015）、加拿大渥太华大学（2015-2023）工作任教。徐晨教授长期从事大数据统计学的基础理论与方法研究，在大数据特征筛选/降维、再抽样理论与方法、分布式统计分析等领域取得系统性创新成果，做出多个原创性贡献。在统计学顶刊JASA、机器学习顶刊IEEE Trans系列及综合类顶刊NSR等国际权威杂志及会议发表研究论文50余篇，其中10余篇发表在影响因子大于10的高影响力刊物上。主持中加多项国家级科研项目；与海内外诸多统计学者有良好的合作关系；积极为社会培养本\硕\博\博后各层次科研人才。团队的科研成果成功应用于基因组学、公众卫生、信息通信、地理遥感、市场营销等多个领域。

学术组织任职

Associate Editor, Journal of the American Statistical Association (2023 – 至今)

Associate Editor, Electronic Journal of Statistics (2023 – 至今)

Associate Editor, The Canadian Journal of Statistics (2019-2021)

科研项目

任务负责人，国家重点研发计划 -“非独立同分布大数据统计基础理论、方法与应用”（2023-2028）

课题负责人，国家重点研发计划 -“数据与机理融合的大数据统计推断”（2022-2027）

研究骨干，国家自然科学基金重大项目 – “大数据的统计学基础与分析方法”（2017-2022）

项目负责人，加拿大自然科学与工程探索基金 – “On the Feasibility of Distributed Statistical Learning for Big Data”(2016-2024)

学术及科研成果、专利、论文

代表性论文：

  1. Jing, K., Khalili, A., Xu, C. (2024+) Class-specific Joint Feature Screening for Ultrahigh-dimensional Mixture Regressions. Journal of the American Statistical Association. To appear.
  2. Li, X.  and Xu, C. (2024) Feature Screening with Conditional Rank Utility for Big-data Classification. Journal of the American Statistical Association, 119, 1385-1395.
  3. Xu, C., Xu, W, Jing, K. (2023) Fast Algorithms for Singular Value Decomposition and Inverse of Nearly Low-rank Matrices. National Science Review, 10(6), 1-4.
  4. Yang, S., Zhang, L., Yu, H., Xu, C., Fan, J., Xu, Z. (2022) Massive Data Clustering by Multi-scale Psychological Observations. National Science Review, 9(2), 1-9.
  5. Zou, B., Jiang, H., Xu, C., Xu, J., You, X. Tang, Y. (2022) Learning Performance of Weighted Distributed Learning with Support Vector Machines. IEEE Transactions on Cybernetics, 53, 4630-4641.
  6. Xia, Z., Chen, Y. and Xu, C. (2022) Multiview PCA: A Methodology of Feature Extraction and Dimension Reduction for High-order Data. IEEE Transactions on Cybernetics, 52, 11068-11080.
  7. Zhang, F., Wang, J. Wang, W. and Xu, C. (2021) Low-tubal-rank plus Sparse Tensor Recovery with Prior Subspace Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 3492-3507.
  8. Zhou. T., Zhu, L., Xu, C. and Li, R. (2020) Model-free Forward Regression via Cumulative Divergence. Journal of the American Statistical Association, 115, 1393-1405.
  9. Li, X., Li, R., Xia, Z. and Xu, C. (2020) Distributed Feature Screening via Componentwise Debiasing. Journal of Machine Learning Research, 21, 1-32.
  10. Gong, T., Xi, Q., Xu, C. (2020) Robust Gradient-based Markov Subsampling. The Thirty-Fourth AAAI Conference on Artificial Intelligence. New York.
  11. Xu, J., Xu, C., Zou, B., Tang Y., Peng, J. You, X. (2019) New Incremental Learning Algorithm with Support Vector Machines. IEEE Transactions on Systems, Man and Cybernetics: Systems, 49, 2230-2241.
  12. Wang, J., Xu, C., Yang, X. and Zurada, J. (2018) A Novel Pruning Algorithm for Smoothing Feed-forward Neural Networks based on Group Lasso. IEEE Transactions on Neural Networks and Learning Systems, 29, 2012-2024.
  13. Zou, B., Xu, C., Lu, Y., Tang, Y., Xu, J. and You, X. (2018). K-Times Markov Sampling for SVMC. IEEE Transactions on Neural Networks and Learning Systems, 29, 1328-1341.
  14. Xu, C., Zhang, Y., Li, R. and Wu, X. (2016). On the Feasibility of Distributed Kernel Regression for Big Data. IEEE Transactions on Knowledge and Data Engineering, 28, 3041 - 3052.
  15. Xu, C. and Chen, J. (2014). The Sparse MLE for Ultra-high-dimensional Feature Screening. Journal of the American Statistical Association, 109, 1257-1269.

软件：

Zang, Q., Xu, C. and Burkett, K. (2024) SMLE: Joint Feature Screening via Sparse MLE. R

package version 2.1-1, URL https://cran.r-project.org/web/packages/SMLE/index.html.

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