Room: STM 555
Office: 613-562-5800 ext. 2030
Work E-mail: cx3@uOttawa.ca
Dr. Xu’s research is in sparse modeling and statistical learning. His interests include feature selection, regularization methods, high-dimensional regression, kernel methods, 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.
- Li, X., Li, R., Xia, Z. and Xu, C. (2020) Distributed Feature Screening via Componentwise Debiasing. Journal of Machine Learning Research. In press.
- Zhou. T., Zhu, L., Xu, C. and Li, R. (2019). Model-free Forward Regression via Cumulative Divergence. Journal of the American Statistical Association. In press.
- 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.
- Xu, C., Lin, S., Fang J. and Li, R. (2016). Prediction-based Termination Rule for Greedy Learning with Massive Data. Statistica Sinica, 26, 841-860.
- 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.
- Xu, C. and Chen, J. (2014). The Sparse MLE for Ultra-high-dimensional Feature Screening. Journal of the American Statistical Association, 109, 1257-1269.
Editorial Service: Associate Editor (2019-present), The Canadian Journal of Statistics
Research Group(s): Statistics and Probability, Data Science and Machine Learning Group
I have limited positions for project BSc and MSc every year. If you are interested in joining my research team, please send me your CV along with your transcript.
If you are currently enrolled in the coursed-based program and would like to transfer to a project/thesis based MSc under my supervision, please contact me near the end of your 2nd term. Priority will be given to the students who showed passion and did well in my courses.
I would be happy to provide reference letters for my students, but only for those ones I know well. If you took my courses before but I do not actually know you in person, it would be very hard for me write a strong letter for you; a plain letter would hurt your application.