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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.

Negar Kiyavash is Willett Faculty Scholar at the University of Illinois and a joint Associate Professor of Industrial and Enterprise Engineering and Electrical and Computer Engineering. She is also affiliated with the Coordinated Science Laboratory (CSL) and the Information Trust Institute. She received her Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2006. Her research interests are in design and analysis of algorithms for network inference and security. She is a recipient of NSF CAREER and AFOSR YIP awards and the Illinois College of Engineering Dean's Award for Excellence in Research.​

Prof. Ashutosh Nayyar

a_timing_approach_to_causal_network_inference.txt · Last modified: 2016/09/01 19:15 (external edit)