معرفی
Xin Tong is an Associate Professor in the Department of Data Sciences and Operations at the Marshall School of Business, University of Southern California. His research focuses on asymmetric statistical learning, Neyman-Pearson classification, high-dimensional statistics, and network analysis. He holds a PhD in Operations Research from Princeton University (2012) and a B.S. in Mathematics from the University of Toronto (2007).
Key contributions include developing Neyman-Pearson classification frameworks for imbalanced datasets and advancing spectral clustering techniques. His work has been supported by NSF grants and NIH funding. Tong serves as an Associate Editor for the Journal of the American Statistical Association and Journal of Business and Economic Statistics.
Recent research highlights include studies on label-noise-adjusted classification algorithms, citation network analysis, and fairness-adjusted machine learning. He teaches courses on modern statistical learning and GenAI/ML strategies for business transformation.
Notable awards include the Zellner Thesis Award (2013) and multiple NSF grants. His lab's work bridges statistical theory with applications in genomics, social networks, and healthcare risk assessment.
