/ Publications & Research
This collection documents peer-reviewed research publications that originated through graduate research at the University of Cincinnati College of Engineering and Applied Science and helped establish the technical foundations behind Convergent Analytics.
Research in topological data analysis, persistent homology, high-performance computing, distributed computing, streaming analytics, and machine learning directly informs the methodology used for industrial analytics, process optimization, predictive maintenance, and operational intelligence engagements.
Incremental Critical Cells for Homology Characterization
Nicholas O. Malott, Anurag Yadav, Philip A. Wilsey • Machine Learning, Optimization, and Data Science (LOD 2025)
An incremental framework for identifying critical topological structures and characterizing homology in evolving datasets.
Piecewise Computation of Persistent Homology
Rohit P. Singh, Nicholas O. Malott, Philip A. Wilsey • IEEE Big Data
A scalable framework for decomposing persistent homology computations into smaller components that can be processed more efficiently.
Scalable Homology Classification through Decomposed Euler Characteristic Curves
Nicholas O. Malott, Philip A. Wilsey • IEEE Big Data
A scalable classification framework using decomposed Euler characteristic curves as efficient topological descriptors for machine learning and pattern recognition tasks.
Generating High Dimensional Test Data for Topological Data Analysis
Rohit P. Singh, Nicholas O. Malott, Blake Sauerwein, Neil McGrogan, Philip A. Wilsey • Bench 2023 / Lecture Notes in Computer Science
A methodology for generating synthetic high-dimensional datasets for benchmarking and evaluating topological data analysis algorithms.
Computation of Persistent Homology on Streaming Data using Topological Data Summaries
Anindya Moitra, Nicholas O. Malott, Philip A. Wilsey • Computational Intelligence
Methods for computing persistent homology in streaming environments through topological summaries that reduce computational cost while preserving meaningful topological structure.
Homology-Separating Triangulated Euler Characteristic Curve
Nicholas O. Malott, Robert R. Lewis, Philip A. Wilsey • IEEE International Conference on Data Mining (ICDM)
A novel Euler characteristic curve construction capable of separating homology classes while maintaining computational efficiency relative to traditional persistent homology techniques.
A Survey on the High-Performance Computation of Persistent Homology
Nicholas O. Malott, Shangye Chen, Philip A. Wilsey • IEEE Transactions on Knowledge and Data Engineering
A comprehensive survey of computational approaches for persistent homology, covering algorithmic complexity, distributed computation, memory constraints, and scalability challenges in topological data analysis.
Data Reduction and Feature Isolation for Computing Persistent Homology on High Dimensional Data
Rishi R. Verma, Nicholas O. Malott, Philip A. Wilsey • IEEE Big Data Workshop
Methods for isolating significant features and reducing dimensional complexity in topological analysis workflows.
Distributed Computation of Persistent Homology from Partitioned Big Data
Nicholas O. Malott, Rishi R. Verma, Rohit P. Singh, Philip A. Wilsey • IEEE Cluster
A distributed framework for computing persistent homology on partitioned datasets, enabling scalable topological analysis of large data collections.
Topology Preserving Data Reduction for Computing Persistent Homology
Nicholas O. Malott, Aaron M. Sens, Philip A. Wilsey • IEEE Big Data
A framework for reducing dataset size while preserving topological structure relevant to persistent homology computations.
Persistent Homology on Streaming Data
Anindya Moitra, Nicholas O. Malott, Philip A. Wilsey • IEEE ICDM Workshops
An approach for applying persistent homology techniques to continuously generated data streams through topological summaries and incremental processing.
Fast Computation of Persistent Homology with Data Reduction and Data Partitioning
Nicholas O. Malott, Philip A. Wilsey • IEEE Big Data
Methods for reducing computational complexity in persistent homology through topology-preserving reduction and partitioning strategies.
Cluster-based Data Reduction for Persistent Homology
Anindya Moitra, Nicholas O. Malott, Philip A. Wilsey • IEEE Big Data
A clustering-based strategy for reducing dataset complexity while preserving topological structure required for persistent homology analysis.