Lingzhou Xue

Professor
Lingzhou Xue
Lingzhou
Xue
Professor
Interests
High-Dimensional Data
Statistical Learning
Graphical Models
Network Analysis
Nonparametric Statistics
Large-Scale Optimization
Address
Phone
(814) 865-1290
Website
Personal Site

Biography

Lingzhou Xue is a Professor of Statistics at Penn State. He received his B.Sc. in Statistics from Peking University in 2008 and his Ph.D. in Statistics from the University of Minnesota in 2012. He was a postdoctoral research associate at Princeton University from 2012-2013. 

His research interests include high-dimensional statistics, nonparametric statistics, statistical and machine learning, large-scale optimization, and statistical modeling in biomedical, environmental, and social sciences. His recent research focuses on causal inference, federated learning, graphical models, high-dimensional inference, optimal transport, random objects, and reinforcement learning. 

He has advised 16 Ph.D. students (nine have graduated) and three postdocs, including four advisees who became tenure-track faculty in statistics. 

He became an Elected Fellow of the Institute of Mathematical Statistics (IMS) in 2024, an Elected Fellow of the American Statistical Association (ASA) in 2023, and an Elected Member of the International Statistical Institute (ISI) in 2016. He received the inaugural Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award in 2021, the inaugural Bernoulli Society New Researcher Award in 2019, and the International Consortium of Chinese Mathematicians Best Paper Award in 2019.

Currently, he serves as an Associate Editor for the Journal of the American Statistical Association, Annals of Applied Statistics, Stat, ACM Transactions on Probabilistic Machine Learning, and Data Science in Science.

 

Selected Honors and Awards

  • Institute of Mathematical Statistics (IMS) Fellow, 2024.
  • American Statistical Association (ASA) Fellow, 2023.
  • National Institute of Statistical Sciences (NISS) Distinguished Service Award, 2023.
  • COPSS Emerging Leader Award, 2021.
  • Adobe’s Data Science Research Award, 2020.
  • International Consortium of Chinese Mathematicians (ICCM) Best Paper Award, 2019.
  • Bernoulli Society (BS) New Researcher Award, 2019.
  • International Chinese Statistical Association (ICSA) International Conference Young Researcher Award, 2016.
  • International Statistical Institute (ISI) Elected Member, 2016. 

 

Selected Publications

  • Zheng, Z., Zhang, H. and Xue, L. (2025) Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition. The Thirteenth International Conference on Learning Representations (ICLR). (Spotlight)
  • Zheng, Z., Zhang, H. and Xue, L. (2025) Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost. The Thirteenth International Conference on Learning Representations (ICLR).
  • Bhattacharjee, S., Li, B. and Xue, L. (2025) Nonlinear Global Fréchet Regression for Random Objects via Weak Conditional Expectation. The Annals of Statistics, 53: 117-143.
  • Zhang, Q., Xue, L. and Li, B. (2024) Dimension Reduction for Fréchet Regression. Journal of the American Statistical Association, 119: 2733-2747.
  • Zheng, Z., Gao, F., Xue, L., and Yang, J. (2024) Federated Q-Learning: Linear Regret Speedup with Low Communication Cost. The Twelfth International Conference on Learning Representations (ICLR).
  • Liu, B., Zhang, Q., Xue, L., Song, P. X. K., and Kang, J. (2024) Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis. Journal of the American Statistical Association, 119: 715-729.
  • Yu, X., Li, D. and Xue, L. (2024) Fisher's Combined Probability Test for High-Dimensional Covariance Matrices. Journal of the American Statistical Association, 119: 511-524.
  • Tao, J., Li, B. and Xue, L. (2024) An Additive Graphical Model for Discrete Data. Journal of the American Statistical Association, 119: 368-381.
  • Zheng, Z., Ma, S. and Xue, L. (2024) A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval. IEEE Transactions on Signal Processing, 72: 1081-1093.
  • Yu, X., Yao, J. and Xue, L. (2024) Power Enhancement for Testing Multi-Factor Asset Pricing Models via Fisher's Method. Journal of Econometrics, 239: 105458.
  • Yu, X., Li, D., Xue, L., and Li, R. (2024) Power-Enhanced Simultaneous Test of High-Dimensional Mean Vectors and Covariance Matrices with Application to Gene-Set Testing. Journal of the American Statistical Association, 118: 2548-2561.
  • Luo, W., Xue, L., Yao, J. and Yu, X.(2022) Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors. Biometrika, 109: 473–487.
  • Wang, B., Ma, S. and Xue, L.. (2022) Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over Stiefel Manifold. Journal of Machine Learning Research, 23: 1−33.
  • Srinivasan, A., Xue, L. and Zhan, X. (2021) Compositional Knockoff Filter for FDR Control in Microbiome Regression Analysis. Biometrics, 77: 984-995.
  • Zou, H. and Xue, L. (2018) A Selective Overview of Sparse Principal Component Analysis. Proceedings of the IEEE, 106: 1311-1320.
  • Fan, J., Xue, L. and Yao, J. (2017) Sufficient Forecasting Using Factor Models. Journal of Econometrics, 201: 292-306.
  • Fan, J., Xue, L. and Zou, H. (2016) Multi-Task Quantile Regression Under the Transnormal Model. Journal of the American Statistical Association, 111: 1726-1735.
  • Fan, J., Xue, L. and Zou, H. (2014) Strong Oracle Optimality of Folded Concave Penalized Estimation. The Annals of Statistics, 42: 819-849.
  • Xue, L. and Zou, H. (2012). Regularized Rank-Based Estimation of High-Dimensional Nonparanormal Graphical Models. The Annals of Statistics, 40(5): 2541-2571.
  • Xue, L., Zou, H. and Cai, T. (2012). Nonconcave Penalized Composite Conditional Likelihood Estimation of Sparse Ising Models. The Annals of Statistics, 40(3): 1403-1429.
  • Xue, L., Ma, S. and Zou, H. (2012).Positive-Definite L1-Penalized Estimation of Large Covariance Matrices. Journal of the American Statistical Association, 107: 1480-1491.
  • Xue, L. and Zou, H. (2011). Sure Independence Screening and Compressed Random Sensing. Biometrika, 98(2): 371-380.

 

Whitepapers

  • Rridgeway, G, Rosenberger, J and Xue, L. (2020) A Call for Statisticians to Engage in Gun Violence Research. (link). Statistics Serving Society, National Institute of Statistical Sciences.
  • Rudin, C, Dunson, D, Irizarry, R, Ji, H, Laber, E, Leek, J, McCormick, T, Rose, S, Schafer, C, van der Laan, M, Wasserman, L and Xue, L. (2014) Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society. (link) Science Policy and Advocacy, American Statistical Association.