University of Illinois, USA 
Title: A Timing Approach to Causal Network Inference
Summary: One of the paramount challenges of this century is that of understanding complex, dynamic, large-scale networks. Such high-dimensional networks, including communication, social, financial, and biological networks, cover the planet and dominate modern life. In this talk, we propose novel approaches to inference in such networks, using timing as an underutilized degree of freedom that provides rich information. We present a framework for learning the structure of the directed information graphs. These graphs are a new type of probabilistic graphical model based on directed information that succinctly capture casual dynamics among random processes in stochastic networks. In the presence of large data, we propose algorithms that identify optimal or near-optimal approximations to the topology of the network.