Shan Shan is an applied mathematician and statistician. Her research and teaching focus on extracting stable and interpretable information from high-dimensional data. On the theory side, she builds algorithms and probabilistic models on geometric objects for data analysis tasks, e.g. dimension reduction, inference, regression. On the applied side, she uses her work on anatomical surfaces (typically, teeth and bones of primates) to gain insights about evolutionary processes. On the implementation side, she develops robust and easy-to-use software to bridge the gap between research and practice.
Assistant Professor of Statistics; on leave Fall 2021
Mathematical framework for machine learning, Bayesian inference, high-dimensional data analysis