University of Southern California

Xin Tong
Assistant Professor of Data Sciences and Operations

USC Marshall School of Business
Los Angeles, CA 90089-0808

PhD, Princeton University; BS, University of Toronto


Xin Tong's research interests are in the areas of high dimensional statistical inference, learning theory, and social and economic networks. He has published papers in journals that include Journal of the Royal Statistical Society: Series B and the Journal of Machine Learning Research. Professor Tong won The Zellner Thesis Award in Business and Economic Statistics 2013.


Tong, X., Feng, Y., and Li, J. (2016) "Neyman-Pearson (NP) classification algo- rithms and NP receiver operating characteristics,".
Fan, J., Qi, L., and Tong, X. (2016) "Penalized least squares estimation with weakly dependent data.," Science China Mathematics,, 59.
Li, J., and Tong, X., "Genomic Applications of Neyman-Pearson Classification Paradigm," in Wang, K., eds., Big Data Analytics in Genomics, Springer (New York)., New York 2016.
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, 149-158.