Xiaotong T. Shen

School of Statistics
University of Minnesota 
Minneapolis, MN 55455

Office Phone:  (612) 624-7098
E-mail:   xshen@umn.edu

Xiaotong T. Shen is the John Black Johnston Distinguished Professor in the College of Liberal Arts at the University of Minnesota. His areas of interest include machine learning and data science, high-dimensional inference, non/semi-parametric inference, causal relations, graphical models, explainable Machine Intelligence (MI), personalization, recommender systems, natural language processing, generative modeling, and nonconvex minimization. His current research efforts are devoted to further developing causal and constrained inference, generative inference and prediction for black-box learners, and diffusion, normalizing flows, and summarization. The targeted application areas are biomedical sciences, artificial intelligence, and engineering.

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Selected recent publications (Supported by NSF and NIH grants)

• Shen, X., Liu, Y., and Shen, R. (2023). Boosting Data Analytics with Synthetic Volume Expansion.   https://arxiv.org/abs/2310.178482305
• Liu, Y., Shen, R., and Shen, X. (2023). Novel Uncertainty Quantification through Perturbation-Assisted Sample Synthesis. IEEE Transactions on Pattern Analysis and Machine Intelligence. Rev 1.   https://arxiv.org/abs/2305.18671
• Yang, Y. and Shen, X. (2023). Boosting Summarization with Normalizing Flows and Optimized Training. Proceeding of the 2023 Conference on Empirical Methods in Natural Language Processing.   https://arxiv.org/abs/2311.00588
• Dai, B., Shen, X., and Pan, W. (2024). Significance Tests of Feature Relevance for a Black-box Learner. IEEE Transactions on Neural Networks and Learning Systems. 35(2), 1898-1911.   https://doi.org/10.1109/TNNLS.2022.3185742 Python library dnn-inference   https://dnn-inference.readthedocs.io/en/latest/
• Li, C., Shen, X., and Pan, W. (2023). Inference for a Large Directed Graphical Model with Interventions. Journal of Machine Learning Research. 24, 1−48. DOI:   https://jmlr.org/papers/volume24/21-0855/21-0855.pdf Code: https://github.com/chunlinli/intdag https://github.com/chunlinli/intdag
• Xue, H., Shen, X., and Pan, W. (2023). Causal inference in Transcriptome-Wide Association Studies with invalid instruments and GWAS summary data. Journal of American Statistical Association. DOI:   https://doi.org/10.1080/01621459.2023.2183127 Software   https://github.com/xue-hr/TScML
• Li, C., Shen, X., and Pan, W. (2023). Nonlinear Causal Discovery with Confounders. Journal of American Statistical Association. DOI:   https://dx.doi.org/10.1080/01621459.2023.2179490
• Bi, X. and Shen, X. (2023). Distribution-invariant privatization. Journal of Economics Theory. 235(2), 444-453. DOI:   https://doi.org/10.1016/j.jeconom.2022.05.004
• Dai, B., Shen, X., Li, C., Chen, L. Y., and Pan, W. (2023). Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation. Annals of Applied Statistics. 17(3), 2019-2038 DOI:   https://dx.doi.org/10.1214/22-AOAS1705
• Shen, X., Bi, X., and Shen, R. (2022). Data Flush. Harvard Data Science Review. 4.2. Milestones and Millstones: Foundations, Theories, and Methods. DOI:   https://dx.doi.org/10.1162/99608f92.681fe3bd
• Liu, M., Shen, X., and Pan, W. (2022). Deep reinforcement learning for personalized treatment recommendation. Statistics in Medicine. DOI:   http://doi.org/10.1002/sim.9491
• Dai, B., Shen, X., and Wong, W. H. (2022). Coupled Generation. Journal of American Statistical Association. 117, 1243--1253. DOI:   https://doi.org/10.1080/01621459.2020.1844719
• Dai, B., Shen, X., and Wang, J. (2022). Embedding Learning. Journal of American Statistical Association. 117, 307-319. DOI:   https://doi.org/10.1080/01621459.2020.1775614
• Xue, H., Shen, X., and Pan, W. (2021). Constrained Maximum Likelihood-based Mendelian Randomization Robust to Both Correlated and Uncorrelated Pleiotropic Effects. The American Journal of Human Genetics. 108(7), 1251-1269. DOI:   https://doi.org/10.1016/j.ajhg.2021.05.014
• Dai, B., Shen, X., Wang, J, and Qu, P. (2021). Scalable Collaborative Ranking for Personalized Prediction. Journal of American Statistical Association. 116, 1215-1223. DOI:   https://doi.org/10.1080/01621459.2019.1691562
• Che, Y., Shen, X., and Zhu. Y. (2020). Multilabel Classification with Multivariate Time Series Predictors. IEEE Transactions on Signal Processing. 68, 5696-5705. DOI:   https://doi.org/10.1109/TSP.2020.3027277
• Wu, C., Xu, G., Shen, X., and Pan, W. (2020). A regularization-based adaptive test for high-dimensional generalized linear models. The Journal of Machine Learning Research. 21(128), 1-67.   http://jmlr.org/papers/v21/18-807.html
• Li, C., Shen, X., Pan, W. (2020). Likelihood ratio tests for a large causal network. Journal of American Statistical Association. 115, 1304-1319. DOI:   https://dx.doi.org/10.1080/01621459.2019.1623042 R-package   https://cran.r-project.org/web/packages/clrdag/index.html
  
• Li, M., Shen, X., and Pan, W. (2019). Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images. GenEpi. 43, 330-341. DOI: http://dx.doi.org/10.1002/gepi.22182
  
• Dai, B., Wang, J, Shen, X., and Qu, P. (2019). A smooth neighborhood recommender system. The Journal of Machine Learning Research. 20(16), 1-24.   http://jmlr.org/papers/v20/17-629.html
  
• Zhu, Y., Shen, X., and Pan, W. (2019). Maximum likelihood inference for a large precision matrix. Journal of American Statistical Association. 112, 1-14. DOI: http://dx.doi.org/10.1080/01621459.2018.1540986
  
• Yuan, Y., Shen, X., Pan, W., and Wang, Z. (2019). Reconstruction of a directed acyclic Gaussian graph. Biometrika. 106, 109-125. DOI: http://dx.doi.org/10.1093/biomet/asy057 R-package   https://github.com/ciryiping/TLPDAG
  
• Bi, X., Qu, P., and Shen, X. (2018). A multilayer tensor factorization with applications to recommender systems. The Annals of Statistics. 46, 3308-3333. DOI: https://doi.org//10.1080/01621459.2016.1219261
  
• Liu, B, Wu, C., Shen, X., and Pan, W. (2017). A novel and efficient algorithm for de novo discovery mutated driver pathways in cancer. Annals of Applied Statistics. 11, 1481-1512. https://dx.doi.org/10.1214/17-AOAS1042
  
• Wang, J., Shen, X., Sun, Y., and Qu, P. (2017). Automatic tagging by key words and phrases. Biometrika. 104(2), 273-290. https://doi.org/10.1093/biomet/asx016
  
• Wu, C., Kwon, S., Shen, X., and Pan, W. (2016). New algorithm and theory for penalized regression-based clustering. The Journal of Machine Learning Research. 17, 1−25. https://jmlr.csail.mit.edu/papers/volume17/15-553/15-553.pdf R-package   https://cran.r-project.org/web/packages/prclust/index.html
  
• Yang, T., Liu, J., Gong, P., Zhang, R., Shen, X., and Ye, J. (2016). Absolute Fused Lasso and its application to genome-wide association studies. The KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1955-1964. DOI: http://dx.doi.org/10.1145/2939672.2939827.
• Xuan, B., Qu, P., Wang, J. and Shen, X. (2016). A group-specific recommender system. Journal of American Statistical Association. DOI: http://dx.doi.org/10.1080/01621459.2016.121926.
• Zhu, Y., Shen, X., and Ye, C. (2016). Personalized prediction and sparsity pursuit in latent factor models. Journal of the American Statistical Association. 109, 1683-1696. DOI:http://dx.doi.org/10.1080/01621459.2014.999158.
• Liu, B., Shen, X., and Pan, W. (2015). Integrative and regularized principal component analysis of multiple sources of data. Statistics in Medicine. DOI: 10.1002/sim.6866.
• Wang, J., Shen, X., Qu, P, and Sun, Y. (2015). Classification with unstructured predictors with an application to sentiment analysis. Journal of the American Statistical Association. DOI: 10.1080/01621459.2015.1089771.
• Yang, S., Lu, Z.., Shen, X., Wonka, P., and Ye, J. (2015). Fused multiple graphical Lasso. SIAM Journal on Optimization. 25, 916-943. DOI: 10.1137/130936397.
• Xiang, S, Shen, X., and Ye, J. (2015). Efficient sparse group feature selection via continuous and discrete optimization. Artificial Intelligence. 224, 28-50. DOI: //doi.org/10.1016/j.artint.2015.02.008.
• Zhu, Y., Shen, X. and Pan, W. (2014). Structural pursuit over multiple undirected graphs. Journal of the American Statistical Association. DOI:10.1080/01621459.2014.921182.
R-package   https://cran.r-project.org/web/packages/MGGM/MGGM.pdf

• Kim, J., Wonzniak, J., Mueller, B, Shen, X., and Pan, W. (2014). Comparison of statistical tests for group differences in brain functional networks. NeuroImage. DOI:10.1016/j.neuroimage.2014.07.031.
• Zhang, Y., Xu, Z., Pan, W., and Shen., X. (2014). Testing for association with multiple traits in generalized estimation equations,with application to neuroimaging data. NeuroImage. 96, 309-325.
• Pan, W., Shen, X. and Liu, B. (2013). Cluster analysis: Unsupervised learning via supervised learning with a noncovex penalty. The Journal of Machine Learning Research. 14, 1865-1889.
• Zhu, Y., Shen, X., and Pan, W. (2013). Simultaneous grouping pursuit and feature selection in regression over an undirected graph. Journal of the American Statistical Association. 108, 713-725.
• Shen, X., Pan, W., Zhu, Y., and Zhou, H. (2013). On constrained and regularized high-dimensional regression. The Annals of the Institute of Statistical Mathematics. 1, 1-26.
• Shen, X., Huang, H., and Pan, W. (2012). Simultaneous supervised clustering and feature selection over a graph. Biometrika, 99, 899-914.
R-package   http://cran.r-project.org/web/packages/FGSG/index.html

• Shen, X., Pan, W., Zhu, Y. (2012). Likelihood-based selection and sharp parameter estimation. Journal of American Statistical Association. 107, 223-232.
R-package (glmtlp)   http://https://cran.r-project.org/web/packages/glmtlp/index.html

• Xing, S., Zhu, Y., Shen, X., and Ye, J. (2012). Optimal exact rank minimization for noisy data. KDD'12, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 480-488.
  
• Shen, X., and Wasserman, L. (2001). Rates of convergence of posterior distributions. The Annals of Statistics, 687-714.
  

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Postdoc positions available (Search Start: 07/01/2003

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Conferences/Workshops

• Conference on Statistical Learning and Data Science, Chapel Hill, 2016
• Midwest Statistics Research Colloquium, Madison, 2012
• Current challenges in statistical learning, Banff International Research Station, Canada, 2011

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