Computer Science Colloquium: Tim Weninger, Ph.D
Principled Structure Discovery from Graph Data
Because of their ability to represent natural phenomena, graphs have been studied extensively in various computing and scientific scenarios. Arguably the most prescient task in the study of graphs is the identification, extraction, and representation of the small substructures that, in aggregate, describe the underlying dynamics encoded by the graph. These extracted models contain the LEGO-like building blocks of real-world graphs, and their overarching goal is to enable in-depth scientific analysis and make predictions about the data. In support of these goals, this presentation will to describe principled techniques that find meaningful graphical substructures and other patterns that are hidden in real world graphs. Then, these graphical patterns will be used to gain insights into the mechanisms that underlie graph structure and evolution.
Tim Weninger is an Associate Professor at the University of Notre Dame where he directs the Data Science Group and is a member of the Interdisciplinary Center for Networks Science and Applications (ICENSA). He has authored over 60 research publications in the areas of social media, machine learning and network science. The key application of his research is to identify how humans generate, curate and search for information in the pursuit of knowledge. He uses properties of these emergent networks to reason about the nature of relatedness, membership and other abstract and physical phenomena. He is a recipient of the NSF CAREER award, the Army Research Office Young Faculty Award, and has received research grants from the Air Force Office of Scientific Research, DARPA, USAID, and the John Templeton Foundation. He is an inaugural member of the ACM's Future of Computing Academy and serves on numerous scientific program committees and editorial boards.
Thursday, October 10 at 2:00 PM
Psychology and Computer Science, 253
100 Normal Rd, DeKalb, IL 60115