In theory there is no difference between theory and practice. In practice there is. -Yogi Berra
Our group combines 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.
Most of our current research focuses on improving the understanding of intermediate-scale network structure, and using it to develop new efficient (parameterized) algorithms for rigorous, robust comparison, anomaly detection, sampling, and feature approximation in real-world graph data.