Xin Tong

Assistant Professor of Data Sciences and Operations
Building
BRI
Room / Office
310A

PhD, Princeton University; BS, University of Toronto

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.
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Xin Tong () "Imbalanced classification: an objective-oriented review ,".
Jessica Li, Xin Tong, Peter Bickel () "Generalized R2 Measures for a Mixture of Bivariate Linear Dependences ,".
Vivian Li, Shan Li, Xin Tong, Ling Deng, Hubin Shi, Jessica Li () "AIDE: annotation-assisted isoform discovery and abundance estimation from RNA-seq data ,"  Genome Research .
Xin Tong, Lucy Xia, Jiacheng Wang, Yang Feng () " Neyman-Pearson classification: parametrics and power enhancement ,"  Journal of Machine Learning Research (accepted).
Xin Tong, Yang Feng, Jingyi Li () "Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristics ,"  Science Advances .
Xin Tong, Jingyi Li () "Discussion of "Random-projection ensemble classification" by Cannings, T.I. and Samworth, R.J. ,"  79.
Jianqing Fan, Lei Qi, Xin Tong () "Penalized least squares estimation with weakly dependent data. ,"  Science China Mathematics,  59.
Jingyi Li, Xin Tong () "Genomic Applications of Neyman-Pearson Classification Paradigm ,"  Big Data Analytics in Genomics  Springer (New York)., 4.
Jianqing Fan, Yang Feng, Jiancheng Jiang, Xin Tong () "Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classification ,"  Journal of the American Statistical Association   111, 149-158.