Shuyi Zhang

• 2014.9-2019.7, Ph.D. in Statistics, Guanghua School of Management, Peking University, Advisor: Prof. Song Xi Chen

• 2010.9-2014.7, B.S. in Mathematics and Applied Mathematics, School of Mathematical Sciences, Beijing Normal University, Advisor: Prof. Zenghu Li

• Mathematics Genealogy

• 2019.9-2020.9, Postdoctoral Fellow, Department of Statistics, Harvard University, Advisor: Prof. Zheng Tracy Ke

• 2018.11-2019.3, Visiting Scholar, Department of Statistics, Iowa State University

• 2016.9-2017.9, Visiting Scholar, Department of Statistics, Iowa State University

• 2015.8-2016.1, Visiting Scholar, Department of Statistics, Iowa State University

Shuyi Zhang is an assistant professor at Academy of Statistics and Interdisciplinary Sciences, Faculty of Economics and Management, East China Normal University. She was a postdoctoral research fellow of the Department of Statistics at Harvard University, and a joint Ph.D. in Statistics in Guanghua School of Management at Peking University and in the Department of Statistics at Iowa State University. 

Her research interests include statistical methods for big data analysis, semi-supervised learning, high-dimensional statistics, environmental risk measurement. 

• Member of The International Environmetrics Society (TIES), 2015-present

• Member of International Chinese Statistical Association (ICSA), 2016-present

• 《Environmetrics》: Associate Editor, 2024 - present

Statistical methods for big data analysis

Semi-supervised learning

High-dimensional statistics

Environmental risk measurement

Postgraduate course: Applied Multivariate Analysis

Undergraduate course: Smart Statistics

• Shanghai Pujiang Program, Principle Investigator, 2021.10-2024.9

Publications (* corresponding author, # alphabetical order, student)

[13] Chen, D., Ke, Z. and Zhang, S.* (2024). VALISE: A robust vertex hunting algorithm. Statistica Sinica, online.  

[12] Su, J., Zhang, S.* and Zhou, Y.* (2024). Solving the missing at random problem in semi-supervised learning: An inverse probability weighting method. Stat, 13(3): e707. [main] [supplement]

[11] Zhou, Y., Lei, B. and Zhang, S. (2024). Transfer learning and its applications in financial big data. China Journal of Econometrics, accepted. [Chinese]

[10] Chen, S.X., Qiu, Y. and Zhang, S. # (2023). Sharp optimality for high-dimensional covariance testing. The Annals of Statistics, 51(5): 1921-1945. [main] [supplement]

[9] Chen, L., Wan, A., Zhang, S., and Zhou, Y. # (2023). Distributed algorithms for U-statistics-based empirical risk minimization. Journal of Machine Learning Research, 24(263): 1−43.  [main] [supplement] 

[8] Tan, T., Zhang, S.* and Zhou, Y. (2023). Improved efficiency of semiparametric estimation for two-sample comparison in semi-supervised learning framework. The Canadian Journal of Statistics, accepted. 

[7] Zhang, S., Chen, S.X. and Qiu, Y. (2023). Mean test for high-dimensional time series. Statistica Sinica, online. 

[6] Zhou, Y.，Zhang, S.* and Li, Z. (2023). Communication-efficient algorithms for quantile regression and their applications in the framework of big data analysis. Journal of Management Sciences in China, 26(5): 70-102. [Chinese] [main]

[5] Zhang, S., Chen, S.X., Guo, B., Wang, H., and Lin, W. (2020). Regional air-quality assessment that adjusts for meteorological confounding. SCIENCE CHINA Mathematics, 50(4): 527-558. [Chinese] [main] [English version]

[4] Chen, L., Guo, B., Huang, J., He, J., Wang, H., Zhang, S., and Chen, S.X. (2018). Assessing air-quality in Beijing-Tianjin-Hebei region: The method and mixed tales of PM2.5 and O3. Atmospheric Environment, 193: 290-301. [main]

[3] Zhang, S., Guo, B., Dong, A., He, J., Xu, Z., and Chen, S.X. (2017). Cautionary tales on air-quality improvement in Beijing. Proceedings of the Royal Society A, 473(2205): 20170457. [main] [supplement]

[2] Liang, X., Li, S., Zhang, S., Huang, H., and Chen, S.X. (2016). PM2.5 data reliability, consistency and air quality assessment in five Chinese cities. Journal of Geophysical Research: Atmospheres, 121(17): 10220-10236. [main] [supplement]

[1] Liang, X., Zou, T., Guo, B., Li, S., Zhang, H., Zhang, S., Huang, H., and Chen, S.X. (2015). Assessing Beijing's PM2.5 pollution: severity, impacts of weather, APEC and winter heating. Proceedings of the Royal Society A, 471(2182): 20150257. [main] [supplement]

Under Revision & Submitted

[1] Su, J., Zhang, S. and Zhou, Y. (2024+). Semi-supervised learning with missing data: A novel approach, under review. 

[2] Su, J., Zhang, S. and Zhou, Y. (2024+). Semi-supervised learning through a single-index projection, under review. 

[3] Li, Z., Zhang, S. and Zhou, Y. (2024+). Multi-source fusion learning for quantile risk minimization under semi-supervised block-wise missing data, under review. 

[4] Li, Z., Pan, S., Zhang, S. and Zhou, Y. (2024+). Rank-based semiparametric efficient estimator for general copula models, under review. 

Patents

China Patent ZL 2018 1 1183512.0: Air quality assessment methods, equipments, devices and storage. Chen, S.X., Zhang, S., Liang, X., October 15, 2019.

Air Quality Assessment Reports

[4] Regional Air Quality Assessment of the Beijing-Tianjin-Hebei Region. [Air Quality Assessment Report (IV)]

[3] Regional Air Quality Assessment of Beijing. [Air Quality Assessment Report (III)]

[2] Statistical Analysis on Five Chinese Cities. [Air Quality Assessment Report (II)]

[1] Statistical Analysis of Beijing. [Air Quality Assessment Report (I)]

More reports: https://songxichen.com/index.php/Research#tab_34