The research interests of the Statistical Science Laboratory encompass a wide range of advanced topics in statistics and machine learning. These include networks, point processes, Bayesian inference, high-dimensional and nonparametric statistics, tree algorithms, and graphical models.

Additionally, the lab is dedicated to improving the interpretability, robustness, and fairness of statistical and machine learning methods. Significant efforts are directed towards counterfactual and Bayesian analysis to develop methodologies that are not only powerful and accurate but also transparent, reliable, and equitable. This comprehensive approach aims to create tools and frameworks that can be trusted and effectively applied in various domains.