Xin Tong, Ph.D.

Assistant Professor

Department of Data Sciences and Operations
Marshall School of Business
University of Southern California
Los Angeles, CA 90089

Office: BRI 310A
Mailing address: BRI 308
Email: xint AT
Phone: 213-740-7348

Marshall Statistics Seminar: Webpage

  • 2012    Ph.D. in Operations Research    Princeton University
  • 2007    B.S. in Mathematics                   The University of Toronto

Research Interests:
  • Neyman-Pearson classification
  • High-dimensional statistics
  • Social and economic networks

Selected Publications

  1. Tong, X.*, Feng, Y. and Li, J.J. (2017).  Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristics (NP-ROC). Science Advances in press.
  2. Tong, X.* and Li, J.J. (2017).  Discussion of "Random-projection ensemble classification" by Cannings, T.I. and Samworth, R.J. Journal of the Royal Statistical Society: Series B 79(4):1025-1026.
  3. Zhao, A., Feng, Y., Wang, L., and Tong, X.*(2016). Neyman-Pearson classification under high-dimensional settings. Journal of Machine Learning Research, 17(213):1−39.
  4. Fan, J., Qi, L., and Tong, X.* (2016). Penalized least squares estimation with weakly dependent data. Science China Mathematics, 59, DOI: 10.1007/s11425-016-0098-x (special issue in memory of Professor Xiru Chen).
  5. Li, J.J. and Tong, X. (2016). Genomic applications of the Neyman-Pearson classification paradigm. Chapter in Big Data Analytics in Genomics. Springer (New York). DOI: 10.1007/978-3-319-41279-5; eBook ISBN: 978-3-319-41279-5.
  6. Tong, X.*, Feng, Y. and Zhao, A. (2016). A survey on Neyman-Pearson classification and suggestions for future research. Wiley Interdisciplinary Reviews: Computational Statistics, 8, 64-81. 
  7. Fan, J., Feng, Y., Jiang, J., and Tong, X. (2016). Feature augmentation via nonparametrics and selection (FANS) in high dimensional classification. Journal of the American Statistical Association111275-287.
  8. Fan, J., Tong, X.*, and Zeng, Y. (2015). Multi-agent inference in social networks: a finite population learning approach. Journal of the American Statistical Association, 110, 149-158.
  9. Tong, X.* (2013). A plug-in approach to Neyman-Pearson classification. Journal of Machine Learning Research14, 3011-3040.
  10. Fan, J., Feng, Y., and Tong, X.* (2012). A road to classification in high dimensional space: the regularized optimal affine discriminant. Journal of the Royal Statistical Society: Series B74, 745-771.
  11. Rigollet, P. and Tong, X. (2011). Neyman-Pearson classification under a strict constraint. The Conference on Learning Theory (COLT).
  12. Rigollet, P. and Tong, X. (2011). Neyman-Pearson classification, convexity and stochastic constraints. Journal of Machine Learning Research, 12, 2825-2849.
    * corresponding author

Selected Awards
  • 2016    R01 GM120507 (Co-Investigator), National Institutes of Health
  • 2016    DMS 1613338 (Principal Investigator), National Science Foundation
  • 2014    Zumberge Individual Fund, University of Southern California
  • 2013    The Zellner Thesis Award in Business and Economic Statistics, American Statistical Association
  • 2011    Laha Travel Award, Institute of Mathematical Statistics