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: HOH 504
Mailing address: BRI 401
Email: xint AT marshall.usc.edu
Phone: 213-740-7348

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

Research Interests:
  • Statistical learning theory
  • High-dimensional classification
  • Social and economic networks

Selected Publications
  1. 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. 
  2. Fan, J., Feng, Y., Jiang, J., and Tong, X. (2015), Feature augmentation via nonparametrics and selection (FANS) in high dimensional classification, Journal of the American Statistical Association, accepted.
  3. 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.
  4. Tong, X.* (2013), A plug-in approach to Neyman-Pearson classification, Journal of Machine Learning Research14, 3011-3040.
  5. 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.
  6. Rigollet, P. and Tong, X. (2011) Neyman-Pearson classification under a strict constraint, the Conference on Learning Theory (COLT).
  7. 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
  • 2014    Zumberge Individual Fund, University of Southern California
  • 2013    The Zellner Thesis Award in Business and Economic StatisticsAmerican Statistical Association
  • 2011    Laha Travel AwardInstitute of Mathematical Statistics