Research

My research falls at the intersection of network science, data mining and machine learning. In particular, my work seeks to identify and understand “higher-order” patterns in network data. This includes both the structure of group interactions (usually modeled with hypergraphs), as well as sequential patterns and dependencies in network data, such as passenger movement through public transit systems, goods through logistics networks, or users navigating the Web. I am also interested in evolutionary game theory and studying the co-evolution of group interaction networks with evolutionary game dynamics to help understand both the dynamic and structural features of important phenomenon, especially altruistic cooperation. Finally, some of my early work used machine learning to improve partially observed (sampled) network data and I remain interested in the limitations of machine learning for solving network problems.

The best way to find my publications is through my Google Scholar page.