Paul L Bendich
Associate Research Professor of Mathematics

I am a mathematician whose main research focus lies in adapting theory from ostensibly pure areas of mathematics, such as topology, geometry, and abstract algebra, into tools that can be broadly used in many data-centered
My initial training was in a recently-emerging field called topological data analysis (TDA). I have been
responsible for several essential and widely-used elements of its theoretical toolkit, with a particular
focus on building TDA methodology for use on stratified spaces. Some of this work involves the
creation of efficient algorithms, but much of it centers around theorem-proof mathematics, using proof techniques
not only from algebraic topology, but also from computational geometry, from probability, and from abstract
algebra. Recently, I have done foundational work on TDA applications in several areas, including to neuroscience, to multi-target tracking, to multi-modal data fusion, and to a probabilistic theory of database merging. I am also becoming involved in efforts to integrate TDA within deep learning theory and practice.

I typically teach courses that connect mathematical principles to machine learning, including upper-level undergraduate courses in topological data analysis and more general high-dimensional data analysis, as well as a sophomore level course (joint between pratt and math) that serves as a broad introduction to machine learning and data analysis concepts.

Current Appointments & Affiliations

Contact Information

Some information on this profile has been compiled automatically from Duke databases and external sources. (Our About page explains how this works.) If you see a problem with the information, please write to Scholars@Duke and let us know. We will reply promptly.