Yanglei Song

Yanglei Song is an Assistant Professor in the Department of Mathematics and Statistics at Queen's University, affiliated with the Faculty of Arts and Science. His research focuses on sequential decision-making problems, including hypothesis testing, change detection, and multi-armed bandits, alongside mathematical statistics such as U-statistics, high-dimensional statistics, and cut-point analysis.

Education:

• Ph.D. in Statistics, University of Illinois at Urbana-Champaign (2019)
• M.Sc. in Mathematics, University of Illinois at Urbana-Champaign (2016)
• B.Eng. in Electronic Engineering, Tsinghua University (2012)

Research Interests:

• Sequential decision-making frameworks with applications in hypothesis testing and stochastic optimization.
• Statistical methodologies for high-dimensional data and nonparametric modeling.
• Development of robust algorithms for reinforcement learning and adaptive systems.
• Mathematical foundations of U-statistics and their computational guarantees.

Publications Trends: Recent work emphasizes theoretical advancements in bandit algorithms, robust model approximation, and statistical inference under covariate-adaptive randomization. Contributions span machine learning, computational statistics, and applied mathematics, with a focus on practical implementations and rigorous theoretical analysis.

Students:

• J. Liu
• Y. Zhou
• Z. Wang
• Z. Zhao
• N. Li
• M. Zhou

Labs/Teams: Maintains an active research group focused on statistical learning and sequential decision-making, with open-source projects such as Stratified incomplete local simplex tests for nonparametric regression analysis.