Blair D. Sullivan

About

Blair D. Sullivan leads a research group combining expertise in structural graph theory, efficient algorithms, and generative network models with tools from geometry and statistics to address fundamental questions in data-driven science. Current research focuses on improving understanding of intermediate-scale network structure and developing efficient parameterized algorithms for comparison, anomaly detection, sampling, and feature approximation in real-world graph data.

Selected publications

• Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied Edges
• Cluster Editing with Vertex Splitting
• A Space-Efficient Algebraic Approach to Robotic Motion Planning