投行分析師讓AI同時跑數據清洗、行業對標和報告初稿，自己只負責審核與決策。律所合夥人用AI幾分鐘完成過去實習生一整天的盡職調查檢索。這些已不是想像。
4 Apr 2026
教學人員
Prof. Xin TONG
經濟學
創新及資訊管理學
Deputy Area Head of Innovation and Information Management
Professor
BGLIS Programme Director
3910 3095
KK 1339
Academic & Professional Qualification
- Ph.D., Princeton University
- B.S., University of Toronto
Biography
Prof. Tong is a Professor of Innovation and Information Management and a Professor of Economics. 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. He currently serves as an Associate Editor for the Journal of the American Statistical Association and the Journal of Business & Economic Statistics.
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 Zellner 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
- Li, X., Han, X., Yang, Q., and Tong, X. (2026) Theoretical Characterization of Generalization in Knowledge Distillation. Accepted by International Conference on Machine Learning (ICML).
- Liang, Z., Xie, T ., Tong, X. and Sesia, M. (2026) Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems. Journal of the American Statistical Association, forthcoming.
- Rava, B., Sun, W., James, G., and Tong, X. (2026) A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification. Journal of the American Statistical Association, forthcoming.
- Cheng, C., Han, X., Tong, X., Wu, Y., Xing, Y. (2026) Degree-Weighted Social Learning. American Economics Journal: Microeconomics, forthcoming.
- Sesia, M., Wang, R. and Tong, X. (2025) Adaptive conformal classification with noisy labels. Journal of the Royal Statistical Society: Series B, 87(3): 796-815.
- Wang, J., 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. (2024) Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data. Journal of the 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 the 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 the 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(212):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.
Recent Publications
4Apr
26Jan
科幻作品常以《終結者》式的暴力對抗描繪人工智慧（AI）的威脅，但現實中AI的侵蝕遠比銀幕敘事更安靜、更隱蔽——它無關肉體毀滅，而關乎存在根本： 一場針對人類「思考權」的系統性讓渡，正在我們與效率的合謀中悄然發生。
26 Jan 2026
教學人員
1Jul
This article develops a conformal prediction method for classification tasks that can adapt to random label contamination in the calibration sample, often leading to more informative prediction sets with stronger coverage guarantees compared to existing approaches. This is obtained through a precise characterization of the coverage inflation (or deflation) suffered by standard conformal inferences in the presence of label contamination, which is then made actionable through a new calibration algorithm. Our solution can leverage different modelling assumptions about the contamination process, while requiring no knowledge of the underlying data distribution or of the inner workings of the classification model. The empirical performance of the proposed method is demonstrated through simulations and an application to object classification with the CIFAR-10H image data set.
1 Jul 2025
Journal of the Royal Statistical Society. Series B: Statistical Methodology