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.

Recent News

  • April 26, 2016

    The Theory in Practice Senior Design Team is presenting at CSC's Annual Posters & Pies (10:30 am - 1:00 pm at the Hunt Library)!

  • March 30, 2015

    We're delighted to announce that Kyle Kloster from Purdue University will be joining Theory In Practice as a postdoc in June 2016.