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:

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.
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.
Wang, J., Xia, L., Bao, Z., and Tong, X.*(2022) Non-splitting Neyman-Pearson Classifiers.
Wang, L. Tong, X., and Wang, R. (2022) Statistics in everyone's backyard: an impact study via citation network analysis, forthcoming at Patterns.
Feng, Y., Tong, X. and, Xin, W. (2021) Targeted crisis control: a Neyman-Pearson approach.
Han, X. Wang, R., and Tong, X.*(2021) Individual-centered partial information in social networks.
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.
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.
Feng, Y., Zhou, M. and Tong, X.*(2021) Imbalanced classification: an objective-oriented review. forthcoming at Statistical Analysis and Data Mining.
Li, J.J., Tong, X., and Bickel, P. (2020) Generalized Pearson correlation squares for a mixture of bivariate linear dependences .
Li, W.V., Tong, X., and Li, J.J. (2020) Bridging cost-sensitive and Neyman-Pearson paradigms for asymmetric binary classification.
Li, J.J. and Tong, X. (2020) Statistical hypothesis testing versus machine-learning binary classification: distinctions and guidelines. Patterns, 1(7):1-10.
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.
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.
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..
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.
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.
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.
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.
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.
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.
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.
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.
Tong, X.* (2013). A plug-in approach to Neyman-Pearson classification. Journal of Machine Learning Research, 14:3011-3040.
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.
Rigollet, P. and Tong, X. (2011) Neyman-Pearson classification under a strict constraint. The Conference on Learning Theory (COLT).
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

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