Basic Information:
Siming Zheng is an Assistant Professor at the School of Statistics and Data Science, Southeast University. He graduated from City University of Hong Kong with a Doctoral Degree in Management Science between 2019 and 2021, and from the University of Chinese Academy of Sciences with a Doctoral Degree in Statistics between 2016 and 2021. He obtained a Bachelor’s Degree in Mathematics and Applied Mathematics from South China University of Technology between 2012 and 2016. He served as a Research Fellow at the National University of Singapore from 2025 to 2026, as a Postdoctoral Research Fellow at Yale University from 2023 to 2025, and as a Postdoctoral Research Fellow at The Chinese University of Hong Kong from 2021 to 2023.
Research area:
His main research areas include machine learning, deep learning and their mathematical foundations, including but not limited to generative learning theory and its applications in statistical problems, deep learning-based statistical learning and risk management methods, as well as the statistical theory and applications of Transformer.
Selected Papers:
[1]Zhihuang Yang, Siming Zheng*and Niansheng Tang (2025+). Supervised Predictive Modeling of High-dimensional Data with Group l0-norm Constrained Neural Networks. Journal of Computational and Graphical Statistics, in press.
[2]Dongxiao Han#, Siming Zheng#, Guohao Shen, Xinyuan Song, Jian Huang and Liuquan Sun* (2025). Deep Mutual Density Ratio Estimation with Bregman Divergence and Its Applications. Journal of the American Statistical Association, 120(551), 1990-2001.
[3]Zhihuang Yang,Siming Zhengand Niansheng Tang* (2025). Efficient Estimation of Single-index Models with Deep ReQU Neural Networks. Acta Mathematica Sinica, English Series, 41, 640–676.
[4]Siming Zheng, Alan T.K. Wan* and Yong Zhou (2024). Semiparametric Recovery of Central Dimension Reduction Space with Nonignorable Nonresponse. Statistica Neerlandica, 78(2), 374–396.
[5]Siming Zheng, Alan T.K. Wan and Yong Zhou* (2023). Missing Data Analysis with Sufficient Dimension Reduction. The Canadian Journal of Statistics, 51(2), 630-651.
[6]Siming Zheng, Juan Zhang and Yong Zhou* (2023). Likelihood Identifiability and Parameter Estimation with Nonignorable Covariate Missing Data. The Canadian Journal of Statistics, 51(4), 1190-1209.
[7]Siming Zheng, Jing Qin and Yong Zhou* (2021). Effect Assessment of Age and Gender on the Incubation Period of COVID-19 with Mixture Regression Model. Journal of Data Science, 19(2), 253-268.
