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Dynamic Clustering, Coverage and Aggregation: a resource allocation approach

In this talk we present a computational framework for solving a large class of dynamic clustering, coverage control and aggregation problems, ranging from those that arise in deployment of mobile sensor networks, to identification of ensemble spike trains in neuronal data, to Markov chain reduction. This framework provides the ability to identify natural clusters in an underlying dynamic data set, and allows us to address inherent trade-offs such as those between cluster resolution and computational cost. More specifically, we define the problem of minimizing an instantaneous coverage metric as an optimization problem using a Maximum Entropy Principle formulation, constructed specifically for the dynamic setting. Locating cluster centers and tracking their associated dynamics is cast as a control design problem that ensures the algorithm achieves progressively better coverage with time.

Type of Seminar:
IfA Seminar
Prof. Carolyn Beck
Department of Industrial and Enterprise Systems Engineering, University of Illinois, Urbana-Champaign
May 17, 2013   3:15 pm

ETZ E 6, Gloriastr. 35
Contact Person:

John Lygeros
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Biographical Sketch:
Carolyn Beck received her Ph.D. (Electrical Engineering) from California Institute of Technology in 1996, her M.S. (Electrical and Computer Engineering) from Carnegie Mellon University in 1985, and her B.S. (Electrical and Computer Engineering) from California State Polytechnic University in 1984. From 1985 through 1989 she was a Research and Development Engineer for Hewlett-Packard in Silicon Valley. She now holds the position of Associate Professor at the University of Illinois at Urbana-Champaign in the Department of Industrial and Enterprise Systems Engineering, and previously has held visiting positions at Stanford University and Lund University. Currently she is a visiting researcher at KTH. Prof. Beck has been the recipient of national research awards in the US, the NSF CAREER Award in 1998 and the ONR Young Investigator Award in 2001. Her research interests lie in the development of model reduction and control analysis theory, combinatorial optimization methods, and learning theory, with applications to biomedical engineering and networks.