Samuel Wang

Y. Samuel Wang is Assistant Professor in the Department of Statistics and Data Science at Cornell University. His research focuses on discovering interpretable structures in high-dimensional data, particularly in causal discovery, graphical models, and mixed membership models. His work develops statistical methods for causal inference in complex datasets.

Wang's research addresses challenges in causal discovery including unobserved confounding, non-Gaussian data, and high-dimensional settings. His publications appear in leading statistics and machine learning venues, covering topics such as functional graphical models, robust inference, and applications in scientific collaborations.