Many large networks emerge from the interactions of individual agents. Online social networks, gene regulatory networks, and the human brain are some important examples. This talk will discuss a framework using probabilistic graphical models for analyzing observational time-series to identify and characterize the local interactions that underlie the complex, global system dynamics.
Chris Quinn is an Assistant Professor in the School of Industrial Engineering at Purdue University. He received his PhD from the department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC).
A flyer can be downloaded here.