CMS/EE Special Seminar
Abstract: One of the paramount challenges of this century is that of understanding complex, dynamic, large-scale networks. Such high-dimensional networks, including social, financial, and biological networks, cover the planet and dominate modern life. In this talk, we propose novel approaches to inference in such networks, for both active (interventional) and passive (observational) learning scenarios. We highlight how timing could be utilized as a degree of freedom that provides rich information about the dynamics. This information allows resolving direction of causation even when only a subset of the nodes is observed (latent setting). In the presence of large data, we propose algorithms that identify optimal or near-optimal approximations to the topology of the network.
Biography: Negar Kiyavash is Willett Faculty Scholar at the University of Illinois and a joint Associate Professor of Industrial and Enterprise Engineering (IE) and Electrical and Computer Engineering (ECE). She is the director of Advance Data Analytics Program in IE and is further affiliated with the Coordinated Science Laboratory (CSL) and the Information Trust Institute. She received her Ph.D. degree in ECE 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.