Reconstruction of Interconnectedness in Networks of Dynamical Systems Based on Passive and Partial Observations

ABSTRACT: Determining interrelatedness structure of various entities from multiple time series data is of  significant interest to many areas. Knowledge of such a structure can aid in identifying cause and effect  relationships, clustering of similar entities, identification of representative elements and model reduction.  In this talk, a methodology for identifying the interrelatedness structure of dynamically related time series  data based on passive observations will be presented. The framework will allow for the presence of loops  in the connectivity structure of the network. Network topology reconstruction based on  optimization/filtering approaches will be complemented with tools from graphical models with notions of  independence posed by d-separation. Results on the extent of the recovery of the network structure  where only a subset of entities are observed and under corruption of data will be presented. For a large  class of systems, methods for exact recovery of the topology under the presence of latent nodes using  results on decomposition of matrices as a sum of low-rank and sparse matrices will be presented and  validated on power system applications.

BIO: Professor Salapaka is in the area of Control and Dynamical Systems. He obtained his Bachelors  degree in Mechanical Engineering from Indian Institute of Technology, Madras in 1991. He obtained his  Masters and PhD. degrees from University of California, Santa Barbara in the years 1993 and 1997  respectively. He was at Electrical Engineering department at Iowa State University from 1997-2007. He is  currently a faculty in the Electrical and Computer Engineering Department at University of Minnesota at  Minneapolis where he holds the Vincentes-Hermes Luh Chair. He is the recipient of the NSF CAREER  Award for the year 1998 and is an IEEE Fellow. His research interests span, controls and systems theory  and its applications to nanotechnology, single molecule physics and power systems.

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