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  • 郑泽敏
  • 特任教授
  • zhengzm@ustc.edu.cn
  • 统计与金融系
  • 概率与统计
English

工作及教育经历

2017年 - 至今,中国科学技术大学,管理学院 (统计与金融系),教授

2015年 - 2017年,中国科学技术大学,管理学院 (统计与金融系),副研究员

2010年 - 2015年,南加州大学,应用数学专业,博士

2006年 - 2010年,中国科学技术大学,数学与应用数学专业,学士


研究兴趣

高维统计推断,统计机器学习及相关的大数据问题



荣誉奖项

中国科学院优秀导师2022

中国科大海外校友基金会青年教师事业奖, 2018

福布斯中国U30(30位30岁以下)精英榜2017

中组部创新人才计划, 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



主要学术论文通讯作者*

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 ComputingDOI: 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.* (2022). 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.


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