Fast Computation of Persistent Homology with Data Reduction and Data Partitioning

This peer-reviewed conference publication was produced as part of graduate research conducted at the University of Cincinnati College of Engineering and Applied Science.

The research explores strategies for reducing the computational burden of persistent homology through data reduction and partitioning techniques. The objective was to preserve meaningful topological information while significantly improving performance.

The resulting framework helped establish a foundation for subsequent work on scalable topological data analysis, distributed computation, and large-scale engineering datasets.