Thursday, September 11 2025, 4 - 5pm Caldwell Hall, Room 204 Jordan Eckerrt Abstract: Prototype selection has become a large area of current research in statistical learning. We introduce a novel topological data analysis (TDA)-based framework for selecting representative prototypes from large datasets. We show that this approach preserves classification performance while substantially reducing data size. Such methods are crucial in resource-constrained environments where memory and computation are limited. Together, these contributions advance both algorithmic and geometric aspects of prototype learning and offer practical tools for scalable, interpretable, and potentially efficient classification. Bio: Dr. Jordan Eckert is a Visiting Assistant Professor at Auburn University, specializing in graph-based statistical learning, topological data analysis, differential privacy, and the intersection of these topics. He received his Ph.D. in Statistics from Auburn recently in August 2025. His work has been recognized with many different grants spanning from prediction of parimutuel betting markets to classification of intermediate range missiles, but he primarily focuses on development of classification and prototype selection methods under difficult data intrinsic characteristics. His presentation today is a small portion of his overall dissertation work, and will be focused on using topological data analysis as a tool for prototype selection. This is joint worth with Dr. Elvan Ceyhan and Dr. Hal Schenck.
