Paul L Bendich

Paul L Bendich is an Adjunct Professor of Mathematics at Duke University, Department of Mathematics. His research focuses on adapting theoretical mathematics, such as topology, geometry, and abstract algebra, into practical tools for data-centered applications. His work includes topological data analysis (TDA), with an emphasis on stratified spaces and algorithm development. He has contributed to TDA applications in neuroscience, multi-target tracking, multi-modal data fusion, and probabilistic database merging. He also explores integrating TDA within deep learning. He teaches courses connecting mathematical principles to machine learning, including topological data analysis and high-dimensional data analysis.

Paul Bendich is a Research Professor at Duke University and Chief Scientist at Geometric Data Analytics, Inc. His research focuses on Topological and Geometric Data Analysis, Machine Learning, and Stratified Spaces. Bendich's work explores the application of topological methods to analyze complex data structures, such as brain artery trees and stratified spaces. His publications include studies on persistent homology, robustness, and statistical behavior classifiers. He also investigates the integration of topological analysis with machine learning for tasks like cover song identification and metric dimensionality reduction. His research bridges geometry and topology to develop tools for data analysis and pattern recognition.