Shuangning Li

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. Her research focuses on advanced statistical methodologies, including causal inference, multiple hypothesis testing, selective inference, and statistical reinforcement learning. Prior to joining Booth, she held a postdoctoral fellowship at Harvard University's Department of Statistics.

• Education:
  • PhD in Statistics from Stanford University, advised by Emmanuel Candès and Stefan Wager
  • Bachelor of Science from the University of Hong Kong

Her academic work intersects with computational statistics and machine learning, addressing challenges in high-dimensional inference and adaptive data analysis. While specific publications are not listed in the provided text, her research trends emphasize robustness and interpretability in statistical learning frameworks.