Hui Zou got his PH.D. in Statistics from Stanford University. He is a full professor at the Univeristy of Minnesota. His primary research interests include statistical learning, flexible statistical modeling and statistical computing. He is a recipient of NSF CAREER Award and the IMS Tweedie Award. He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics and the American Association for the Advancement of Science. From 2014--2019, Hui has been identified as an ISI Highly Cited Researcher in mathematics. From 2020-2022, He is among the World's top 2% scientists ranked by Stanford University's study. 

Honors and Awards

Fellow elected, American Association For The Advancement Of Science

World's top 2% scientists ranked by Stanford University 

Best Paper Award in Applied Mathematics, 2019 ICCM

Fellow of American Statistical Association

Fellow of Institute of Mathematical Statistics

Scholar of the College, 2015

Web of Science Highly Cited Researcher, 2014--2019

Council of Graduate Students Outstanding Faculty Award, 2013

Institute of Mathematical Statistics Tweedie award, 2011

McKnight Presidential Fellow, 2010

NSF career award, 2009

McKnight Land-Grant Professorship, 2008

Books

• Zou, H. (2018). High-dimensional Classification, in Handbook of Big Data Analytics edited by Wolfgang Härdle, Henry Horng-Shing Lu and Xiaotong Shen, Springer. pp. 225–261.

• Zou, H. (2009). A Computable Bound for the Geometric Convergence Rate of the Lasso Gibbs Sampler, in Frontiers of Biostatistics and Bioinformatics (edited by Shuangge Ma and Yuedong Wang), University of Science and Technology of China Press.

• Zhu, J. and Zou, H. (2007). Variable Selection For The Linear Support Vector Machine, In: Chen K., Wang L. (eds) Trends in Neural Computation. Studies in Computational Intelligence, vol 35. Springer, Berlin, Heidelberg.

• Zou, H. and Hastie, T. (2007). Model Building and Feature Selection with Genomic Data, in Computational Methods of Feature Selection(edited by Huan Liu and Hiroshi Motoda), Chapman & Hall/CRC.

Selected Publications

• Hou, X, Mai, Q. and Zou, H. (2023). Tensor Mixture Discriminant Analysis with applications to sensor array data analysis. Annals of Applied Statistics, accepted.

• Kwon, O., Lu, Z. and Zou, H. (2023). Exactly Uncorrelated Sparse Principal Component Analysis. Journal of Computational and Graphical Statistics, https://doi.org/10.1080/10618600.2023.2232843

• Kwon, O. and Zou, H. (2023). Leaky Hockey Stick Loss: The First Negatively Divergent Margin-based Loss Function for Classification. Journal of Machine Learning Research. 24(239), 1-40.

• Zhou, L. , Wang, B. and Zou, H. (2023). Sparse Convoluted Rank Regression in High Dimensions. Journal of the American Statistical Association, In press. https://doi.org/10.1080/01621459.2023.2202433.

• Zhou, H. and Zou, H. (2023). The Nonparametric Box-Cox Model for High-Dimensional Regression Analysis. Journal of Econometrics, In press. https://doi.org/10.1016/j.jeconom.2023.01.025

• Jacobson, T. and Zou, H. (2023). High-dimensional Censored Regression via the Penalized Tobit Likelihood. Journal of Business and Economic Statistics, https://doi.org/10.1080/07350015.2023.2182309.

• Yin, Y, Song, Y. and Zou, H. (2022). A Simple Method for Estimating Gaussian Graphical Models. Statistica Sinica, In press. doi:10.5705/ss.202021.0273

• He, D., Zhou, Y. and Zou, H. (2022). Robust Rank Canonical Correlation Analysis for Multivariate Survival Data. Statistica Sinica, DOI: 10.5705/ss.202022.0069.

• Wang, B. Zhou, L. , Gu,Y. and Zou, H. (2022). Density-Convoluted Support Vector Machines for High-Dimensional Classification. IEEE Transactions on Information Theory, 69(4), 2523–2536.

• Chen, C., Gu, Y., Zou, H. and Zhu, L. (2022). Distributed Sparse Composite Quantile Regression in Ultrahigh Dimensions. Statistica Sinica, DOI: 10.5705/ss.202022.0095

• Mai, Q. He, D. and Zou, H. (2022). Coordinatewise Gaussianization: Theories and Applications. Journal of the American Statistical Association, In press. https://doi.org/10.1080/01621459.2022.244825

• Wang, B. and Zou, H. (2022). Fast and Exact Leave-One-Out Analysis of Large-Margin Classifiers. Technometrics, 64(3), 291–298.

• Zhou, L. and Zou, H. (2021). Cross-fitted Residual Regression for High Dimensional Heteroscedasticity Pursuit. Journal of the American Statistical Association, In press. https://doi.org/10.1080/01621459.2021.1970570

• Wang, B. and Zou, H. (2021). Honest Leave-One-Out Cross-Validation for Estimating Post-Tuning Generalization Error. Stat, DOI: 10.1002/sta4.413

• Jacobson, T. and Zou, H. (2021). Do Predictor Envelopes Really Reduce Dimension? Journal of Data Science, 19(4), 528–541.

• Yin, Y. and Zou, H. (2021). Expectile Regression via Deep Neural Networks. Stat, 10;e315.

• He, D., Zhou, Y. and Zou, H. (2021). On Sure Screening with Multiple Responses. Statistica Sinica, 31, 1749–1777.

• Datta, A. and Zou, H. (2020). A Note on Cross-validation for Lasso under Measurement Errors. Technometrics, 62(4), 549–556.

• Gu, Y. and Zou, H. (2020). Sparse Composite Quantile Regression in Ultrahigh Dimensions with Tuning Parameter Calibration. IEEE Transactions on Information Theory, 66(11), 7132–7154.

• Lang, W. and Zou, H. (2020). A Simple Method to Improve Principal Components Regression. Stat, 9(1);e288.

• Chen, S., Ma, S., Xue, L. and Zou, H. (2020). An Alternating Manifold Proximal Gradient Method for Sparse PCA and Sparse CCA. INFORMS Journal on Optimization, DOI: 10.1287/ijoo.2019.0032

• Zhang, X., Mai, Q. and Zou, H. (2020). The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response. Journal of Machine Learning Research, 21(29):1–36.

• He, D., Zhou, Y. and Zou, H. (2020) High-Dimensional Variable Selection with Right Censored Length-biased Data. Statistica Sinica, 30(1), 193–215.

• Ren, Y. , Lin, L., Lian, Q. Zou, H., and Chu, H. (2019). Real-world Performance of Meta-analysis Methods for Double-Zero-Event Studies with Dichotomous Outcomes Using the Cochrane Database of Systematic Reviews. Journal of General Internal Medicine, DOI: 10.1007/s11606-019-04925-8.

• Gu, Y. and Zou, H. (2019). Aggregated Expectile Regression by Exponential Weighting. Statistica Sinica, 29(2), 671–692.

• Wang, B. and Zou, H. (2019). A Multicategory Kernel Distance Weighted Discrimination Method for Multiclass Classification. Technometrics, 61(3), 396–408.

• Datta, A., Zou, H. and Banerjee, S. (2019). Bayesian High-dimensional Regression for Change Point Analysis. Statistics and Its Interface, 12(2), 253–264.

• Mai, Q., Yang, Y. and Zou, H. (2019). Multiclass Sparse Discriminant Analysis. Statistica Sinica, 29(1), 97–111.

• Zou H. (2019). Classification with high dimensional features. Wiley Interdisciplinary Reviews: Computational Statistics, 11 (1), e1453

• Li, D., Xue, L. and Zou, H. (2018). Applications of Peter Hall’s Martingale Limit Theory to Estimating and Testing High Dimensional Covariance Matrices. Statistica Sinica, 28, 2657–2670.

• Zou, H. and Xue, L. (2018). A Selective Overview of Sparse Principal Component Analysis. Proceedings of the IEEE, 106(8), 1311–1320.

• Gu,Y., Fan, J., Kong, L., Ma, S. and Zou, H. (2018). ADMM for High-dimensional Sparse Penalized Quantile Regression. Technometrics, 60(3), 319–331.

• Yang, Y., Qian, W. and Zou, H. (2018). Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models. Journal of Business and Economic Statistics, 36(3), 456–470.

• Wang, B. and Zou, H. (2018). Another Look at Distance Weighted Discrimination. Journal of the Royal Statistical Society, Series B, 80(1), 177–198.

• Yang, Y., Zhang, T. and Zou, H. (2018). Expectile Regression in Reproducing Kernel Hilbert Space. Technometrics, 60(1), 26–35.

• Datta, A. and Zou, H. (2017). CoCoLasso for High-dimensional Error-in-variables Regression. Annals of Statistics, 45(6),1–27.

• Koerner, T. K., Zhang, Y., Nelson, P. B., Wang, B. & Zou, H. (2017). Neural indices of phonemic discrimination and sentence-level speech intelligibility in quiet and noise: A P3 study. Hearing Research, 350, 58–67.

• Fan, J., Liu, H., Yang, N. and Zou, H. (2017). High Dimensional Semiparametric Latent Graphical Model for Mixed Data. Journal of the Royal Statistical Society, Series B, 79(2), 405–421.

• Gu, Y. and Zou, H. (2016). High-dimensional Generalizations of Asymmetric Least Squares Regression and Their Applications. Annals of Statistics, 44(6), 2661–2694.

• Wang, B. and Zou, H. (2016). Sparse Distance Weighted Discriminant. Journal of Computational and Graphical Statistics, 25(3), 826–838.

• Koerner, T. K., Zhang, Y., Nelson, P. B., Wang, B. & Zou, H. (2016). Neural indices of phonemic discrimination and sentence-level speech intelligibility in quiet and noise: A mismatch negativity study. Hearing Research, 339, 40–49.

• Fan, J., Xue, L. and Zou, H. (2016). Multi-task Quantile Regression under The Transnormal Model. Journal of the American Statistical Association, 111(516), 1726–1735.

• Li, D. and Zou, H. (2016). SURE Information Criteria for Large Covariance Matrix Estimation and Their Asymptotic Properties. IEEE Transaction on Information Theory, 62(4), 2153–2169.

• Qian, W., Yang, Y. and Zou, H. (2016). Tweedie’s Compound Poisson Model With Grouped Elastic Net. Journal of Computational and Graphical Statistics, 25(2), 606–625.

• Mai, Q. and Zou, H. (2015). The Fused Kolmogorov Filter: A Nonparametric Model-Free Screening Method. Annals of Statistics, 43(4), 1471–1497.

• Mai, Q. and Zou, H. (2015). Sparse Semiparametric Discriminant Analysis. Journal of Multivariate Analysis, 135, 175–188.

• Mai, Q. and Zou, H. (2015). Nonparametric Variable Transformation in Sufficient Dimension Reduction. Technometics, 57(1), 1–10.

• Yang, Y. and Zou, H. (2015). Nonparametric Multiple Expectile Regression via ER-Boost. Journal of Statistical Computation and Simulation, 85(7), 1442–1458.

• Yang, Y. and Zou, H. (2015). A Fast Unified Algorithm for Solving Group-Lasso Penalized Learning Problems. Statistics and Computing, 25(6), 1129–1141.

• Fan, J., Xue, L. and Zou, H. (2014). Strong Oracle Property of Folded Concave Penalized Estimation. Annals of Statistics, 42(3), 819–849.

• Zhang, T. and Zou, H. (2014). Sparse Precision Matrix Estimation via Lasso Penalized D-Trace Loss. Biometrika, 101(1), 103–120.

• Song, R., Yi, F. and Zou, H. (2014). On Varying-coefficient Independence Screening for High-dimensional Varying-coefficient Models. Statistica Sinica, 24(4), 1735–1752.

• Zou, H. (2014). Generalizing Koenker’s Distribution. Journal of Statistical Planning and Inference, 148, 123–127.

• Xue, L. and Zou, H. (2014). Rank-based Tapering Estimation of Bandable Correlation Matrices. Statistica Sinica, 24(1), 83–100.

• Xue, L. and Zou, H. (2014). Optimal Estimation of Sparse Correlation Matrices of Semiparametric Gaussian Copula. Statistics and Its Interface, 7(2), 201–209.

• Yang, Y. and Zou, H. (2014). A Coordinate Majorization Descent Algorithm for ℓ₁ Penalized Learning. Journal of Statistical Computation and Simulation. 84(1), 84–95.

• Lin, C-Y., Zhang, H.H., Bondell, H. and Zou, H. (2013). Variable Selection for Nonparametric Quantile Regression via Smoothing Spline ANOVA. Stat 2(1), 255–268.

• Ma, S., Xue, L. and Zou, H. (2013). Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection. Neural Computation, 25, 2172–2198.

• Mai, Q. and Zou, H. (2013). A Note On the Connection and Equivalence of Three Sparse Linear Discriminant Analysis Method. Technometrics, 55(2), 243–246.

• Mai, Q. and Zou, H. (2013). The Kolmogorov Filter for Variable Screening in High-dimensional Binary Classification. Biometrika, 100(1), 229–234.

• Xue, L. and Zou, H. (2013). Minimax Optimal Estimation of General Bandable Covariance Matrices. Journal of Multivariate Analysis, 116, 45–51.

• Yi, F. and Zou, H. (2013). SURE-tuned Tapering Estimation of Large Covariance Matrices. Computational Statistics and Data Analysis, 58, 339–351.

• Yang, Y. and Zou, H. (2013). A Cocktail Algorithm for Solving The Elastic Net Penalized Cox’s Regression in High Dimensions. Statistics and Its Interface, 6, 167–173.

• Yang, Y. and Zou, H. (2013). An Efficient Algorithm for Computing The HHSVM and Its Generalizations. Journal of Computational and Graphical Statistics, 22(2), 396–415.

• Xue, L., Ma, S. and Zou, H. (2012). Positive Definite ℓ₁ Penalized Estimation of Large Covariance Matrices. Journal of the American Statistical Association, 107(500), 1480–1491.

• Xue, L. and Zou, H. (2012). Regularized Rank-based Estimation of High-dimensional Nonparanormal Graphical Models. Annals of Statistics, 40(5), 2541–2571.

• Xue, L., Zou, H. and Cai, T. (2012). Non-concave Penalized Composite Conditional Likelihood Estimation of Sparse Ising Models. Annals of Statistics, 40(3), 1403–1429.

• Chen, B., Yu, Y., Zou, H. and Liang, H. (2012). Profiled Adaptive Elastic-Net Procedure for Partially Linear Models with High-dimensional Covariates. Journal of Statistical Planning and Inference, 142(7), 1733–1745.

• Mai, Q., Zou, H., and Yuan, M. (2012). A Direct Approach to Sparse Discriminant Analysis in Ultra-high Dimensions. Biometrika, 99(1), 29–42.

• Xue, L. and Zou, H. (2011). Sure Independence Screening and Compressed Random Sensing. Biometrika, 98(2), 371–380.

• Ruan, L., Yuan, M. and Zou, H. (2011). ℓ₁ Penalized Estimation of High Dimensional Gaussian Mixture Models. Neural Computation, 23(6), 1605–1622.

• Kai, B., Li, R. and Zou, H. (2011). New Efficient Estimation and Variable Selection Methods for Semiparametric Varying-Coefficient Partially Linear Models. Annals of Statistics, 39(1), 305–332.

• Choi, J., Zou, H. and Oehlert, G. (2011). A Penalized Maximum Likelihood Approach to Sparse Factor Analysis. Statistics and Its Interface, 3(4), 429–436.

• Kai, B., Li, R. and Zou, H. (2010). Local CQR Smoothing: An Efficient and Safe Alternative to Local Polynomial Regression. Journal of the Royal Statistical Society, Series B, 72(1), 49–69.

• Yuan, M. and Zou, H. (2009). Efficient Global Approximation of Generalized Nonlinear ℓ₁-Regularized Solution Paths and Its Applications. Journal of the American Statistical Association, 104(488), 1562–1574.

• Yuan, M., Joseph, R. and Zou, H. (2009). Structured Variable Selection and Estimation, Annals of Applied Statistics, 3(4), 1738–1757.

• Zhu, J., Zou, H., Rosset, S. and Hastie, T. (2009) Multi-class AdaBoost. Statistics and Its Interface, 2(3), 349–360.

• Zou, H. and Zhang, H.H. (2009). On the Adaptive Elastic-Net with A Diverging Number of Parameters. Annals of Statistics, 37(4), 1733–1751.

• Zou, H., Zhu, J. and Hastie, T. (2008). New Multicategory Boosting Algorithms Based on Multicategory Fisher-Consistent Losses. Annals of Applied Statistics,2(4), 1290–1306.

• Zou, H. and Yuan, M. (2008). Regularized Simultaneous Model Selection in Multiple Quantiles Regression. Computational Statistics and Data Analysis, 52, 5296–5304.

• Zou, H., Yuan, M. (2008). Composite Quantile Regression and The Oracle Model Selection Theory. Annals of Statistics, 36(3), 1108–1126.

• Zou, H. (2008). A Note on Path-based Variable Selection in The Penalized Proportional Hazards Model. Biometrika, 95, 241–247.

• Zou, H., Li, R. (2008). Rejoinder: One-step Sparse Estimates in the Nonconcave Penalized Likelihood Models. Annals of Statistics, 36(4), 1561–1566.

• Zou, H., Li, R. (2008). One-step Sparse Estimates in the Nonconcave Penalized Likelihood Models (with discussion and a rejoinder from the authors). Annals of Statistics, 36(4), 1509–1566.

• Zou, H. and Yuan, M. (2008). The F_∞-norm Support Vector Machine. Statistica Sinica, 18(1), 379–398.

• Wang, L., Zhu, J. and Zou, H (2008). Hybrid Huberized Support Vector Machines for Microarray Classification and Gene Selection. Bioinformatics, 24(3) 412–419.

• Wu, S., Zou, H. and Yuan, M. (2008). Structured Variable Selection in Support Vector Machines. Electronic Journal of Statistics, Vol. 2, 103–117.

• Zou, H. (2007). An Improved 1-norm Support Vector Machine for Simultaneous Classification and Variable Selection. Eleventh International Conference on Artificial Intelligence and Statistics.

• Zou, H., Hastie, T. and Tibshirani, R. (2007). On the Degrees of Freedom of the Lasso. Annals of Statistics, 35(5) 2173–2192.

• Zou, H. (2006) The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association, 101(476), 1418–1429.

• Zou, H., Hastie, T. and Tibshirani, R. (2006). Sparse Principal Component Analysis. Journal of Computational and Graphical Statistics, 15(2), 265–286.

• Zou, H., Zhu, J., Rosset, S. and Hastie, T. (2006). Automatic Bias Correction Methods in Semi-Supervised Learning. Contemporary Mathematics, 443, 165–175.

• Wang L. Zhu, J. and Zou, H. (2006). The Doubly Regularized Support Vector Machine. Statistica Sinica, 16(2), 589–616.

• Daniels, M., Zhou, Z. and Zou, H. (2006). Conditionally Specified Space-Time Models for Multivariate Processes. Journal of Computational and Graphical Statistics, 15(1), 157–177.

• Zou, H. and Hastie, T. (2005). Regularization and Variable selection via the Elastic Net. Journal of the Royal Statistical Society, Series B. 67(2), 301–320.

• Rosset, S., Zhu, J., Zou, H. and Hastie, T. (2005). A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning. Advances In Neural Information Processing Systems, 17.

• Zou, H. and Yang, Y. (2004) Combining Time Series Models for Forecasting. International Journal of Forecasting, 20(1), 69–84.

• Zou, H. (2020). Ridge regression–Still Inspiring after 50 Years. Technometrics, 62(4), 456–458.

• Zou, H. (2016). Invited Discussion of “Estimating Structured High-Dimensional Covariance and Precision Matrices: Optimal Rates and Adaptive Estimation” by Cai, Ren and Zhou. Electronic Journal of Statistics. 10(1), 60–66.

• Xue, L. and Zou, H. (2013). Invited Discussion of “Large Covariance Estimation by Thresholding Principal Orthogonal Complements” by Fan, Liao and Micheva. Journal of the Royal Statistical Society, Series B, 75(4), 672–674.

• Xue, L. and Zou, H. (2012). Invited Discussion of “Minimax Estimation of Large Covariance Matrices under ℓ₁-Norm” by Cai and Zhou, Statistica Sinica, 22(4), 1349–1354.

• Zou, H. (2010). Invited Discussion of “Stability Selection” by Meichaushen and Bühlmann, Journal of the Royal Statistical Society, Series B, 72(4), 468.

• Zou, H. (2008). Invited Discussion of “Sure Independence Screening for Ultra-high Dimensional Feature Space” by Fan and Lv, Journal of the Royal Statistical Society, Series B, 70(5), 904.