معرفی
Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics.
**Education:**
- Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager)
- Bachelor of Science, University of Hong Kong
**Research Interests:**
- Causal inference in complex systems (e.g., networks, high-dimensional data)
- Statistical methods for experimental design and robustness
- Machine learning applications in genomics and reinforcement learning
- Randomization-based testing and knockoff filters
**Recent Work Trends:**
His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics.
**Advising & Academic Background:**
His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.
