Dr. Yanglei Song

Biography

Yanglei Song is an Assistant Professor in the Department of Mathematics and Statistics at Queen's University. He obtained his Ph.D. in Statistics from the University of Illinois at Urbana-Champaign in 2019, where he also earned an M.Sc. in Mathematics in 2016. His academic background is complemented by a Bachelor of Engineering in Electronic Engineering from Tsinghua University in 2012. His research interests primarily focus on sequential decision-making problems, non-standard asymptotics, and mathematical statistics. He is particularly engaged in change point analysis, which involves detecting shifts in data distributions to identify significant changes in underlying processes. In online settings, he works on methods for quickly identifying changes while minimizing false alarms, addressing the complexities of monitoring data streams with intricate dependency structures. Additionally, Yanglei has explored recent advances in debiased machine learning, demonstrating that valid statistical inference can be performed on estimators generated by machine learning algorithms such as lasso and random forests. He aims to apply these methods across various types of data and to propose inferential procedures that establish robust theoretical properties, with a special focus on data collected through covariate adaptive designs.

Research Interests