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Conference Publication University of Cincinnati CEAS

Incremental Critical Cells for Homology Characterization

Nicholas O. Malott, Anurag Yadav, Philip A. Wilsey
| Machine Learning, Optimization, and Data Science (LOD 2025)
HomologyTopological Data AnalysisComputational Topology

Summary

An incremental framework for identifying critical topological structures and characterizing homology in evolving datasets.

Research Context

This publication reflects graduate research conducted at the University of Cincinnati College of Engineering and Applied Science. It is included here to document the technical foundation behind Convergent Analytics' work in industrial analytics, applied AI, high-performance computing, and topological data analysis.

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

The research investigates incremental approaches for identifying critical cells and characterizing topological structure within evolving datasets. The methodology supports efficient computation while preserving meaningful topological information.

The work contributes to ongoing efforts to make computational topology more scalable, adaptive, and applicable to dynamic analytical environments.

Citation

Malott, N.O., Yadav, A., Wilsey, P.A. Incremental Critical Cells for Homology Characterization. LOD 2025.