Working & Education

2017 -- Present    Professor, The School of Management, University of Science and Technology of China

2015 -- 2017       Associate Professor, The School of Management, University of Science and Technology of China

2010 -- 2015       Ph.D. in Applied Mathematics, University of Southern California

2006 -- 2010       B.S. in Mathematics and Applied Mathematics, University of Science and Technology of China

Research Interests

High-dimensional statistical inference, statistical machine learning, and big data problems

Honors & Awards

Excellent Tutor, Chinese Academy of Sciences, 2022

Junior Faculty Career Award, USTC Overseas Alumni Foundation, 2018

Forbes U30 (30 Under 30) China List, 2017

Innovative Talent Plan, Organization Department of the CPC Central Committee, 2017

CAMS Prize for Excellence in Research, University of Southern California, 2015

IMS Travel Award, Institute of Mathematical Statistics, 2014

Merit Fellowship, USC Dana and David Dornsife College of Letters, Arts and Sciences, 2012-2013

Selected Publications

Chen, K., Dong, R., Xu, W. and Zheng, Z. (2022). Fast stagewise sparse factor regression. Journal of Machine Learning Research 23(271), 1-45.

Zheng, Z., Zhang, J. and Li, Y. (2022). L0-regularized learning for high-dimensional additive hazards regression. INFORMS Journal on Computing, DOI: 10.1287/ijoc.2022.1208.

Zheng, Z., Li, Y., Wu, J. and Wang, Y. (2022). Sequential scaled sparse factor regression. Journal of Business & Economic Statistics 40, 595-604.

Kong, Y., Zhou, J., Zheng, Z., Amaro, H. and Guerrero. E. G. (2021). Using machine learning to advance disparities research: Subgroup analyses of access to opioid treatment. Health Services Research 57, 411-421.

Zhou, J., Li, Y., Zheng, Z. and Li, D. (2022). Reproducible learning in large-scale graphical models. Journal of Multivariate Analysis 189, 104934.

Dong, Y., Li, D., Zheng, Z. and Zhou, J. (2022). Reproducible feature selection in high-dimensional accelerated failure time models. Statistics & Probability Letters 181, 109275.

Zheng, Z., Lv, J. and Lin, W. (2021). Nonsparse learning with latent variables. Operations Research 69(1), 346-359.

Zheng, Z., Zhang, J., Li, Y. and Wu, Y. (2021). Partitioned approach for high-dimensional confidence intervals with large split sizes. Statistica Sinica 31, 1935-1959.

Dong, R., Li, D. and Zheng, Z. (2021). Parallel integrative learning for large-scale multi-response regression with incomplete outcomes. Computational Statistics & Data Analysis 160, 107243.

Wu, J., Zheng, Z., Li, Y. and Zhang, Y. (2020). Scalable interpretable learning for multi-response error-in-variables regression. Journal of Multivariate Analysis 179, 104644.

Zheng, Z., Li, L., Zhou, J. and Kong, Y. (2020). Innovated scalable dynamic learning for time-varying graphical models. Statistics & Probability Letters 165, 108843.

Zheng, Z., Shi, H., Li, Y. and Yuan, H. (2020). Uniform joint screening for ultra-high dimensional graphical models. Journal of Multivariate Analysis 179, 104645.

Zheng, Z., Bahadori, M. T., Liu, Y. and Lv, J. (2019). Scalable interpretable multi-response regression via SEED. Journal of Machine Learning Research 20, 1-34.

Zheng, Z., Li, Y., Yu, C., Li, G.  (2018). Balanced estimation for high-dimensional measurement error models. Computational Statistics & Data Analysis 126, 78-91.

Kong, Y., Zheng, Z. and Lv, J. (2016). The constrained Dantzig selector with enhanced consistency. Journal of Machine Learning Research 17, 1-22.

Fan, Y., Kong, Y., Li, D. and Zheng, Z. (2015). Innovated interaction screening for high-dimensional nonlinear classification. The Annals of Statistics 43, 1243-1272.

Zheng, Z., Fan, Y. and Lv, J. (2014). High-dimensional thresholded regression and shrinkage effect. Journal of the Royal Statistical Society Series B 76, 627-649.

Lv, J. and Zheng, Z. (2014). Discussion: A significance test for the Lasso. The Annals of Statistics 42, 493-500.