Xin Tong, Ph.D.

Associate 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 marshall.usc.edu

Phone: 213-740-7348

Education:

  • 2012 Ph.D. in Operations Research Princeton University

(Advisors: Jianqing Fan and Philippe Rigollet)

  • 2007 B.S. in Mathematics The University of Toronto

Research Interests:

  • Neyman-Pearson classification

  • High-dimensional statistics

  • Network and social media

Editorial Services:

  • 2023-2025 associate editor, the Journal of the American Statistical Association, Theory and Methods

  • 2020-2022 associate editor, the Journal of the American Statistical Association, Applications and Case Studies

  • 2020-2024 associate editor, the Journal of Business and Economic Statistics

Research Highlight:

Projects and Publications:

  1. Wang, L., Han, X., and Tong, X. (2022) Skilled mutual fund selection: false discovery control under dependence. forthcoming at Journal of Business and Economic Statistics.

  2. Yao, S., Rava, B., Tong, X. and James, G. (2022) Asymmetric error control under imperfect supervision: a label-noise-adjusted Neyman-Pearson umbrella algorithm. forthcoming at Journal of the American Statistical Association.

  3. Wang, J., Xia, L., Bao, Z., and Tong, X.*(2022) Non-splitting Neyman-Pearson Classifiers.

  4. Wang, L. Tong, X., and Wang, R. (2022) Statistics in everyone's backyard: an impact study via citation network analysis, forthcoming at Patterns.

  5. Feng, Y., Tong, X. and, Xin, W. (2021) Targeted crisis control: a Neyman-Pearson approach.

  6. Han, X. Wang, R., and Tong, X.*(2021) Individual-centered partial information in social networks.

  7. Li, J.J., Chen, Y. and Tong, X.*(2021) A flexible model-free prediction-based framework for feature ranking. forthcoming at Journal of Machine Learning Research.

  8. Han, X., Tong, X.*, and Fan, Y. (2021) Eigen selection in spectral clustering: a theory guided practice. forthcoming at Journal of the American Statistical Association.

  9. Feng, Y., Zhou, M. and Tong, X.*(2021) Imbalanced classification: an objective-oriented review. forthcoming at Statistical Analysis and Data Mining.

  10. Li, J.J., Tong, X., and Bickel, P. (2020) Generalized Pearson correlation squares for a mixture of bivariate linear dependences .

  11. Li, W.V., Tong, X., and Li, J.J. (2020) Bridging cost-sensitive and Neyman-Pearson paradigms for asymmetric binary classification.

  12. Li, J.J. and Tong, X. (2020) Statistical hypothesis testing versus machine-learning binary classification: distinctions and guidelines. Patterns, 1(7):1-10.

  13. Tong, X., Xia, L., Wang, J., and Feng, Y. (2020) Neyman-Pearson classification: parametrics and sample size requirement. Journal of Machine Learning Research, 21(12):1-48.

  14. Xia, L., Zhao, R., Wu, Y., and Tong, X.* (2020) Intentional control of type I error over unconscious data distortion: a Neyman-Pearson approach to text classification. forthcoming at Journal of the American Statistical Association.

  15. Li, W.V., Li, S., and Tong, X., Deng, L., Shi, H. and Li, J.J. (2019) AIDE: annotation-assisted isoform discovery and abundance estimation from RNA-seq data . Genome Research, 29:2056-2072..

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. 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.

  24. Tong, X.* (2013). A plug-in approach to Neyman-Pearson classification. Journal of Machine Learning Research, 14:3011-3040.

  25. 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.

  26. Rigollet, P. and Tong, X. (2011) Neyman-Pearson classification under a strict constraint. The Conference on Learning Theory (COLT).

  27. 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:

  • 2021 DMS 2113500 (Principal Investigator), National Science Foundation

  • 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