Academic & Professional Qualification
- Ph.D., Princeton University
- B.S., University of Toronto
Biography
Professor Xin Tong is a Professor of Information and Innovation Management at the University of Hong Kong (HKU). His research focuses on statistical and machine-learning methods, social and economic networks, AI ethics, and the intersection of AI and social sciences. Notably, he has developed a series of works on Neyman-Pearson classification, addressing asymmetric error importance in applications such as medical diagnosis, loan approval, and cybersecurity. More recently, his research examines the societal impact of AI development.
Professor Tong earned a B.S. in Mathematics with high distinction from the University of Toronto and a Ph.D. in Operations Research from Princeton University, where his dissertation received the Zeller Award in Business and Economic Statistics from the American Statistical Association. He has served as an instructor in MIT’s Department of Mathematics and as a tenured faculty member at the University of Southern California.
Research Interest
- Asymmetry in statistical learning
- Local information in networks
- AI ethics
- Algorithmic market
- Societal impact of AI development
Selected Publications
- Sesia, M., Wang, R. and Tong, X. (2024) Adaptive conformal classification with noisy labels. Journal of the Royal Statistical Society: Series B, qkae114.
- Jing, M., Xia, L., Bao, Z. and Tong, X. (2024) Non-splitting Neyman-Pearson classifiers. Journal of Machine Learning Research, 25(292):1-61.
- Han, X., Wang, R., Yang, Q. and Tong, X. (2024) Individual-centered partial information in social networks. Journal of Machine Learning Research, 25(230), 1-60.
- Li, J.J., Zhou, H.J., Bickel, P. and Tong, X. (2024) Dissecting gene expression heterogeneity: generalized Pearson correlation squares and the K-lines clustering algorithm. Journal of the American Statistical Association, 119(548), 2450-2463.
- Wang, L., Wang, R., Li, J.J. and Tong, X. (2023) Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data. Journal of American Statistical Association, 119(545), 39-61.
- Han, X., Tong, X. and Fan, Y. (2023) Eigen selection in spectral clustering: a theory guided practice. Journal of American Statistical Association, 118(541), 109-121.
- Yao, S., Rava, B., Tong, X. and James, G. (2023) Asymmetric error control under imperfect supervision: a label-noise-adjusted Neyman-Pearson umbrella algorithm. Journal of American Statistical Association, 118(543), 1824-1836.
- Wang, L., Han, X. and Tong, X. (2023) Skilled mutual fund selection: false discovery control under dependence. Journal of Business and Economic Statistics, 41(2), 578-592.
- Wang, L., Tong, X. and Wang, R. (2022) Statistics in everyone’s backyard: an impact study via citation network analysis. Patterns, 3(8):1-13.
- Li, J.J., Chen, Y., and Tong, X. (2021) A flexible model-free prediction-based framework for feature ranking. Journal of Machine Learning Research, 22(124):154.
- Xia, L., Zhao, R., Wu, Y., and Tong, X. (2021) Intentional control of type I error over unconscious data distortion: a Neyman-Pearson approach to text classification. Journal of the American Statistical Association, 116(533):68-81.
- Li, J.J. and Tong, X. (2020) Statistical hypothesis testing versus machine-learning binary classification: distinctions and guideline. Patterns, 1(7):1-10.
- Tong, X., Xia, L., Wang, J., and Feng, Y. (2020) Neyman-Pearson classification: Parametrics and power enhancement. Journal of Machine Learning Research, 21(12):1-48.
- Tong, X., Feng, Y., and Li, J.J. (2018) Neyman-Pearson (NP) classification algo-rithms and NP receiver operating characteristics (NP-ROC). Science Advances, 4(2): eaao1659. (2023IF: 15.4)
- Zhao, A., Feng, Y., Wang, L. and Tong, X. (2016) Neyman-Pearson classification under high-dimensional settings. Journal of Machine Learning Research, 17, (213):1-39.
- Li, J.J., and Tong, X. (2016) Genomic Applications of 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.
- Fan, J., Feng, Y., Jiang, J., and Tong, X. (2016) Feature augmented nonparametrics and selection (FANS) high dimensional classification. Journal of the American Statistical Association, 111, 275-287.
- Fan, J., Tong, X., Zeng, Y. (2015) Multi-agent Learning in Social Networks: a Finite Population Learning Approach. Journal of the American Statistical Association, 110, 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, 745-771.
- Rigollet, P. and Tong, X. (2011) Neyman-Pearson classification, convexity and stochastic constraints. Journal of Machine Learning Research, 12, 2825-2849.