I am an assistant professor at Fudan University. I completed my Ph.D. at Peking University. After that, I pursued postdoctoral research at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. I am a youth editorial board member for the Journal of Systems Science and Complexity.

My research interest includes dynamic online algorithms for complex data (functional data, matrix data, high-dimensional data, etc.) and policy evaluation for spatio-temporal data with interference. I am also interested in incorporating machine learning methods and theory, ordinary/partial differential equations with statistical modeling and inference. My email address is yangying@fudan.edu.cn

🔥 News

📝 Publications

Yang Y. and Yao F.* (2022). Online estimation for functional data. JASA.
- In this work, we propose a dynamic candidate bandwidth method and apply it to functional data analysis for the mean and covariance estimation in the online context.
Yang Y., Yao F.* and Zhao P. (2023). Online smooth backfitting for generalized additive model. JASA.
- We propose an online smoothing backfitting method for generalized additive models coupled with local linear estimation. The idea can be extended to general nonlinear optimization problems.
Chen Z.#, Yang Y.# and Yao F.* (2023). Dynamic matrix recovery. JASA.
- In this article, we propose a general framework for dynamic matrix recovery of low-rank matrices that evolve smoothly over time. We start from the setting that the observations are independent across time, then extend to the setting that both the design matrix and noise possess certain temporal correlation.
Luo S.#, Yang Y.#, Shi C.#, Yao F., Ye J. and Zhu H.* (2024). Policy evaluation for temporal and/or spatial dependent experiments. JRSSB.
- The aim of this article is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments.
Yang Y., Shi C., Yao F., Wang S. and Zhu H.* (2024+). Spatially randomized designs can enhance policy evaluation.
- This article studies the benefits of using spatially randomized experimental designs which partition the experimental area into distinct, non-overlapping units with treatments assigned randomly.
Zhang W.#, Yang Y.# and Yao F. (2024+). Spatial interference detection in treatment effect model.
- We introduce a low-rank and sparse treatment effect model that leverages data-driven techniques to identify the locations of interference effects, which allows for interference with heterogeneous forms.

🎖 Honors and Awards

2024.07 Xianghui Scholars Youth Program.
2023.09 Young Elite Scientists Sponsorship Program from China Association for Science and Technology.
2023.01 Guozhi Xu Posdoctoral Research Foundation.
2022.07 Special Foundation from China Postdoctoral Science Foundation.

📖 Educations

2017.09 - 2022.07, Ph.D. in statistics, Peking University.
2013.09 - 2017.07, Bachelor in statistics, Beijing Normal University.

💬 Invited Talks

International Conference on Econometrics and Statistics, Beijing, China, July 17-19, 2024
IMS Asia Pacific Rim Meeting, Melbourne, Australia, January 4-7, 2024
International Chinese Statistical Association China Conference, Chengdu, China, June 30 – July 3, 2023

💻 Internships