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

Curriculum Vitae

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
  • Network and social media

Research Highlight:


  1. Chen, Y., Li, J.J., and Tong, X.* (2019) Neyman-Pearson criterion (NPC): a model selection criterion for asymmetric binary classification. arXiv:1903.05262.
  2. Li, J.J., Tong, X., and Bickel, P. (2018) Generalized R squares measures for a mixture of bivariate linear dependences . arXiv:1811.09965.
  3. Li, W.V., Li, S., and Tong, X., Deng, L., Shi, H. and Li, J.J. (2018) AIDE: annotation-assisted isoform discovery and abundance estimation from RNA-seq data .
  4. Tong, X., Xia, L., Wang, J., and Feng, Y. (2018) Neyman-Pearson classification: parametrics and power enhancement. arXiv:1802.02557v3.
  5. Xia, L., Zhao, R., Wu, Y., and Tong, X.* (2018) Intentional control of type I error over unconscious data distortion: a Neyman-Pearson approach to text classification. arXiv:1802.02558.
  6. Tong, X.*, Feng, Y. and Li, J.J. (2018) Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristics (NP-ROC). Science Advances, 4(2):eaao1659.
  7. 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.
  8. Zhao, A., Feng, Y., Wang, L., and Tong, X.* (2016) Neyman-Pearson classification under high-dimensional settings. Journal of Machine Learning Research, 17:1−39.
  9. Fan, J., Qi, L., and Tong, X.* (2016) Penalized least squares estimation with weakly dependent data. Science China Mathematics (special issue in memory of Professor Xiru Chen), 59(12):2335-2354.
  10. 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.
  11. 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.
  12. 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 Association, 111(513):275-287.
  13. 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(509):149-158.
  14. Tong, X.* (2013). A plug-in approach to Neyman-Pearson classification. Journal of Machine Learning Research, 14:3011-3040.
  15. 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 B, 74(4):745-771.
  16. Rigollet, P. and Tong, X. (2011) Neyman-Pearson classification under a strict constraint. The Conference on Learning Theory (COLT).
  17. 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