He obtained his doctorate in statistics from the Institute of Statistics and Big Data of Renmin University of China in June 2023, and has been engaged in post doctoral research the Academy of Mathematics and Systems Science (AMSS) of the Chinese Academy of Sciences (CAS) since July 2023. The main research directions are sufficient dimension reduction, distributed learning, causal inference, and false discovery rate control.
Zhengtian Zhu
/Doctor
E-mail：zhengtianzhu AT amss.ac.cn
Awards and honors
Lead research projects
- 2024.07 – present: China Postdoctoral Science Foundation (2024M753435), research on distributed statistical algorithms for sparse and sufficient dimension reduction in the era of big data, from July.
- 2023.12 – present: Postdoctoral Fellowship Program of CPSF(GZC20232914), research on efficient algorithms for sufficient dimension reduction under massive data.
Representative papers
- Zhu, Z., Xu, W. & Zhu, L. (2025). Distributed mean dimension reduction through semi-parametric approaches. Statistica Sinica, 35, 111-129.
- Qiao, N., Chen, C. & Zhu, Z. (2025). Robust and efficient sparse learning over networks: a decentralized surrogate composite quantile regression approach. Statistics and Computing, 35, 24.
- Chen, C., & Zhu, Z. (2024). Byzantine-robust and efficient distributed sparsity learning: a surrogate composite quantile regression approach. Statistics and Computing, 34, 158.
- Zhu, Z., & Zhu, L. (2024). An improved divide-and-conquer approach to estimating mean functional, with application to average treatment effect estimation. Journal of Business & Economic Statistics, 1-10.
- Zhu, Z., & Zhu, L. (2022). Distributed dimension reduction with nearly oracle rate. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15, 692-706.