{"168091":{"#nid":"168091","#data":{"type":"event","title":"CSE Seminar: Leonid Bunimovich, Regents\u0027 Professor of Mathematics","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ECSE Seminar\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESpeaker:\u003C\/strong\u003E Leonid Bunimovich, Regents\u0027 Professor of Mathematics, Georgia Tech\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EHow to compress networks while keeping their important characteristics\u003Cbr \/\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EOne of the fundamental concerns in the study of networks is\u003Cbr \/\u003Eunderstanding the relation between a network\u0027s structure (\u0022topology\u0022) and\u003Cbr \/\u003Eits dynamics (evolution in time). However, the networks we often encounter\u003Cbr \/\u003Ein either nature or engineering are typically very large. It is therefore\u003Cbr \/\u003Etempting to want to reduce such networks by excluding a part of their\u003Cbr \/\u003Eelements while preserving some important characteristic(s) of the original\u003Cbr \/\u003Enetwork. Such fundamental characteristic is the spectrum (collection of all\u003Cbr \/\u003Eeigenvalues) of the network\u0027s weighted adjacency matrix. Moreover, this\u003Cbr \/\u003Ematrix often contains all the known information about a network. Can one\u003Cbr \/\u003Ehope to reduce a network while maintaining its spectrum? It seems that\u003Cbr \/\u003Ethere is no hope because of the Fundamental Theorem of Algebra which says\u003Cbr \/\u003Ethat the spectrum of MxM matrix contains M eigenvalues. Therefore the\u003Cbr \/\u003Espectrum of a smaller matrix (corresponding to a smaller network) contains\u003Cbr \/\u003Efewer eigenvalues. However, it is possible to do and the recently developed\u003Cbr \/\u003Etheory of isospectral networks\u0027 reduction suggests new ways of networks\u0027\u003Cbr \/\u003Eanalysis and synthesis. In particular, a new equivalence relation in the\u003Cbr \/\u003Eclass of all networks was found. Besides (as a \u0022byproduct\u0022) the theory of\u003Cbr \/\u003Eisospectral networks transformations allowed to advance some classical\u003Cbr \/\u003Eareas of Mathematics as e.g. estimation of matrices\u0027 spectra. Another good\u003Cbr \/\u003Enews is that numerical implementation of the procedure of isospectral\u003Cbr \/\u003Ereduction is very simple and straightforward.\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EProf. Leonid Bunimovich is a Regents\u0027 Professor of Mathematics at\u003Cbr \/\u003EGeorgia Tech. His research concerns a wide array of problems at the\u003Cbr \/\u003Eintersection of dynamical systems and statistics. He is known for his\u003Cbr \/\u003Ediscovery of focusing chaotic billiards (the \u0022Bunimovich stadium\u0022) and for\u003Cbr \/\u003Ethe Bunimovich mushroom, a billiard with mixed regular and chaotic\u003Cbr \/\u003Edynamics. He became a Regents\u2019 Professor and given the Exemplary Senior\u003Cbr \/\u003EFaculty Award, in 2000 receiving the Outstanding Faculty Research Author\u003Cbr \/\u003EAward. He was made a Fellow of the Institute of Physics in 2004, and was\u003Cbr \/\u003Enamed a Chartered Physicist and Fellow of the UK Institute of Physics in\u003Cbr \/\u003E1999. He received the Humboldt Prize in 2003. Prof. Bunimovich received his\u003Cbr \/\u003EPhD from the University of Moscow (1973) and a Doctorate of Sciences from\u003Cbr \/\u003Ethe Institute for Theoretical Physics of the Academy of Sciences of USSR,\u003Cbr \/\u003EKiev (1986).\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"How to compress networks while keeping their important characteristics"}],"uid":"27439","created_gmt":"2012-11-02 11:47:24","changed_gmt":"2016-10-08 02:01:06","author":"Lometa Mitchell","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2012-11-09T13:00:00-05:00","event_time_end":"2012-11-09T14:00:00-05:00","event_time_end_last":"2012-11-09T14:00:00-05:00","gmt_time_start":"2012-11-09 18:00:00","gmt_time_end":"2012-11-09 19:00:00","gmt_time_end_last":"2012-11-09 19:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1304","name":"High Performance Computing (HPC)"},{"id":"47223","name":"College of Computing"},{"id":"50875","name":"School of Computer Science"},{"id":"50876","name":"School of Interactive Computing"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EFor more information, please contact Mark Borodovsky \u2013 \u003Ca href=\u0022mailto:borodovsky@gatech.edu\u0022\u003Eborodovsky@gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}