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epidemic_processes_over_topologically_varying_networks [2017/09/29 11:55] (current)
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|+||Title: Epidemic Processes Over Topologically Varying Networks|
|+||The study of epidemic processes has been a topic of interest for many years over a wide range of areas, including mathematical systems, biology, physics, computer science, social sciences and economics. More recently, there has been a resurgence of interest in the study of epidemic processes focused on the spread of viruses over networks, motivated not only by recent devastating outbreaks of infectious diseases, but also by the rapid spread of opinions over social networks, and the security threats posed by computer viruses. Most of the models considered in these recent studies have been focused on network models with static network structures, however almost all systems being considered have inherently dynamic structures. In this talk, we will discuss the modeling of epidemic processes over topologically varying networks, and present stability analysis results which elucidate the behavior of these systems. Specifically, we will derive conditions that guarantee convergence to the disease free equilibrium under varying assumptions on the networks and disease process parameters. Simulation results and potential control actions will be presented and discussed to conclude the talk.|
|+||Carolyn L. Beck is a faculty member in the College of Engineering at the University of Illinois, Urbana-Champaign, in the Department of Industrial and Systems Engineering. She completed her Ph.D. at Caltech, her M.S. at Carnegie Mellon, and her B.S. at Cal Poly, all in Electrical Engineering. Prior to completing her Ph.D., she was an R&D engineer at Hewlett- Packard in Santa Clara. Carolyn has held visiting faculty positions at the Royal Institute of Technology (KTH) in Stockholm, Stanford University, and Lund University in Lund, Sweden.|
|+||She was the recipient of an NSF CAREER award and an ONR Young Investigator award, as well as local teaching awards. Her research interests range from network inference problems to control of anesthetic pharmacodynamics and include mathematical systems theory, model reduction and approximation for the purpose of analysis and control design, and clustering and aggregation methods.|