Learning the Input/Output Structure of a Network from data

March 05, 2021, Zoom

Donatello Materassi

U. Minnesotta, Electrical and Computer Engineering

Abstract

Networks have become ubiquitous in science. Interconnected systems are successfully exploited to perform novel modeling approaches in many fields, such as Economics, Biology, Cognitive Sciences, Ecology and Geology. While networks of dynamical systems have been deeply studied and analyzed in physics and engineering, there is a reduced number of results addressing the problem of reconstructing an unknown network of dynamic systems, since it poses formidable theoretical and practical challenges. One of the main challenges is the identification of networked systems that are difficult to access or manipulate. Thus, the necessity for general tools for the identification of networks that are known only via non-invasive observation is rapidly emerging. The talk addresses this problem under several scenarios trying to form a picture as general and complete as possible.

Speaker's Bio

Donatello Materassi holds a Laurea in "Ingegneria Informatica" and a "Dottorato di Ricerca" in Nonlinear Dynamics and Complex Systems from Universita' degli Studi di Firenze, Italy. He has been a research associate at University of Minnesota (Twin Cities) from 2008 till 2011. He has been concurrently both a post-doctoral researcher at Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology and a lecturer at Harvard University till 2014. Since 2014 he has been an assistant professor at University of Tennessee in Knoxville and since 2019 he is an assistant professor at University of Minnesota in the Department of Electrical and Computer Engineering. In 2016, he was a recipient of the NSF CAREER award. His main research interests are graphical models, stochastic systems and cybernetics.

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