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Xin Tong

  • Associate Professor of Data Sciences and Operations

Xin Tong's current research interests focus on asymmetric statistical learning, addressing challenges in the Neyman-Pearson classification paradigm, data distortion, sampling bias, asymmetric groups in classification and clustering, and partial knowledge in clustering and community detection. He has published papers in journals that include Science Advances, Journal of American Statistical Association, Journal of the Royal Statistical Society: Series B and the Journal of Machine Learning Research. Professor Tong's research has been partially supported by US NSF and NIH.

Xin Tong

Areas of Expertise

Network Analysis


Data Sciences + Operations