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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Abstract:
Networks have become ubiquitous in science. The principal advantages provided by a networked system are a modular approach to design, the possibility of directly introducing redundancy and the realization of distributed and parallel algorithms. All these advantages have led to consider networked systems in the realization of many technological devices. At the same time, it is not surprising that natural and biological systems tend to exhibit strong modularity, as well. 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. A variety of techniques based on Wiener Filtering for the reconstruction of different classes of networks are introduced. Also novel design methodologies are proposed for controllers that can be safely and reliably deployed in a continuously operating network even when the network topology is uncertain and only non-invasive partial observations are available. It is illustrated how these methodologies could become the foundation for the synthesis of local, safe, ready to be deployed controllers providing a framework to realize self-healing networks.
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. In 2016, he was a recipient of the NSF CAREER award. His main research interests are graphical models, stochastic systems and cybernetics.
