Lingzhou Xue is a Professor of Statistics and Associate Director of the National Institute of Statistical Sciences. He is the Core Faculty of the Statistical Learning and Data Mining (SLDM) Lab and the Microbiome Data Science (MDS) Lab.
Xue received his Ph.D. in Statistics from the University of Minnesota in 2012. He received his M.S. in Statistics from the University of Minnesota in 2011 and his B.S. in Statistics from Peking University in 2008.
His research interests include high-dimensional statistics, statistical machine learning, nonparametric statistics, optimization, and statistical applications in biomedical science, business analytics, and public health. His papers have been published in top-tier journals in statistics and data science, including The Annals of Statistics, Biometrics, Biometrika, eLife, INFORMS Journal on Optimization, Journal of the American Statistical Association, Journal of Machine Learning Research, Journal of Accounting & Economics, Journal of Business & Economic Statistics, Journal of Econometrics, Environmental Science & Technology, Monthly Weather Review, Nature Materials, Psychometrika, Proceedings of the IEEE, etc. His research has been supported by the National Science Foundation (NSF), the National Institutes of Health (NIH), and the Health Effects Institute (HEI).
Currently, he serves as an Associate Editor for the Journal of the American Statistical Association, Stat, ACM Transactions on Probabilistic Machine Learning, and Data Science in Science.
Honors and Awards
- American Statistical Association (ASA) Fellow, 2023.
- COPSS Leadership Academy Award For Emerging Leaders in Statistics, 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.
- Du, K, Huddart, S. J., Xue, L. and Zhang, Y. (2020) Using a Hidden Markov Model to Measure Earnings Quality. Journal of Accounting & Economics, in press.
- Chen, Y., Ju, L., Zhou, F., Liao, J., Xue, L., Su, Q., Yuan, Y., Lu, H., Jackson, S. and Zhu C. (2019). An Integrin αIIbβ3 Intermediate Affinity State Mediates Biomechanical Platelet Aggregation. Nature Materials, 18, 760–769.
- 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.
- Ju, L., Chen, Y., Xue, L., Du, X. and Zhu C. (2016). Cooperative Unfolding of Distinctive Mechanoreceptor Domains Transduces Force into Signals. eLife, e15447.
- 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., Ma, S. and Zou, H. (2012).Positive-Definite L1-Penalized Estimation of Large Covariance Matrices. Journal of the American Statistical Association, 107: 1480-1491.