Computing That Serves

Clustering Dynamic Data


Thursday, December 8, 2016 - 11:00am


Carolyn Beck


Sean Warnick

Carolyn Beck
Thursday, December 8, 2016
11:00am  1170 TMCB


We consider the problem of clustering data sets where the data points are dynamic, or essentially time-varying.  Our approach is to incorporate features of both the deterministic annealing algorithm as well as control theoretic methods in our computational solution.  Extensions of our method can be made to the problem of aggregating time-varying graphs, for which we have developed a quantitative measure of dissimilarity that allows us to compare directed graphs of differing sizes. In this talk, an overview of our dynamic clustering algorithm will be given, along with some analysis of the algorithm properties.  We will conclude with  a few highlighted applications, and further extensions as time allows.


Carolyn Beck is currently an Associate Professor in the Department of Industrial and Enterprise Systems Engineering, at the University of Illinois at Urbana-Champaign. Her research activities are focused on the development of model reduction, clustering and aggregation methods, with applications in bioengineering and networks problems. Carolyn has been a visiting faculty at KTH in Stockholm (2013), Stanford University in California (2006) and Lund University in Lund, Sweden (1996). She has received national research awards (NSF CAREER and ONR Young Investigator), and local teaching awards.
Carolyn received her Ph.D. from Caltech, her M.S. from Carnegie Mellon, and her B.S. from California State Polytechnic University, all in Electrical Engineering. Prior to completing her graduate studies, she gained industry experience at Hewlett-Packard in Silicon Valley, where she worked as a Research and Development Engineer for 5 years.