Rate My Professor William Eberle

Eberle holds a Ph.D. in Computer Science (2007) from the University of Texas at Arlington, with a dissertation on information theoretic, probabilistic, and maximum partial substructure algorithms for discovering graph-based anomalies; an M.S. in Computer Science (1991) from the same institution; and a B.A. in Computer Science (1986) from the University of Texas at Austin. His research interests encompass data mining, graph theory, artificial intelligence, and software engineering, with specializations in graph-based anomaly detection applied to domains such as fraud in telecommunications and e-commerce, social networks, healthcare, financial transactions, insider threats, and security. He has received grants from the Department of Homeland Security and the National Science Foundation. Notable publications include 'Anomaly Detection in Dynamic Graphs: A Comprehensive Survey' (2024, with O.A. Ekle), 'Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates' (2025, with O.A. Ekle and J. Christopher), 'Scalable Anomaly Detection in Graphs' (2015, with L. Holder), and 'An Approach for Concept Drift Detection in a Graph Stream Using Discriminative Subgraphs' (2020, with R. Paudel). Eberle has earned the Douglas D. Dankel II Service Award from the Florida Artificial Intelligence Research Society (2023), Teacher-Scholar Awards from Tennessee Technological University (2013, 2014, 2016), Best Paper Award at FLAIRS (2009), and others. He chairs the Computer Science Personnel Committee and serves on the Graduate School Executive Committee, Computer Science Executive Committee, and numerous others, and has edited proceedings for FLAIRS conferences.