(1.)Basic Information

 

Chen Xu

Lingjun Chair Professor (Statistics)

School of Mathematics and Statistics

Room: 207 Math Building

Email: cx3@xjtu.edu.cn

(2.)Positions

Big data analytics, High-dimensional statistics, Statistical learning

(3.)Education

PhD (Statistics) - The University of British Columbia         Vancouver, Canada

MA (Statistics) - York University                                            Toronto, Canada

BSc (Applied Math) – Xi’an Jiaotong University                  Xi’an, China

(4.)Honors

Dr. Xu’s research is in sparse modeling and statistical learning. His interests include feature screening, distributed learning, high-dimensional regression, subsampling, and statistical computing. Recently, he focuses on developing efficient processing methods for big data, where traditional methods are less helpful due to the high computational burden. His works emphasize on both theoretical and computational aspects, which have a wide application scope in various disciplines.

(5.)Scientific Research

Associate Editor, Journal of the American Statistical Association (2023 - present)

Associate Editor, Electronic Journal of Statistics (2023 - 2025)

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

(6.)Teaching

Featured articles

  1. Jing, K., Khalili, A., Xu, C. (2025) Class-specific Joint Feature Screening for Ultrahigh-dimensional Mixture Regressions. Journal of the American Statistical Association. In press.
  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.

Software

Zang, Q., Xu, C. and Burkett, K. (2025) SMLE: Joint Feature Screening via Sparse MLE. R package version 2.2-2. Download

(7.)Contact

We are hiring! The team is expanding at multiple levels:  tenue-track assistant professor, postdoc, PhD student, visiting scholar. Send me your CV, and we will explore new possibilities for you.