Title: Communities in Networks
Abstract: Networks are all around us, from online social networks to business relationships to family and friends. The patterns in these connections control the way that information, ideas, and diseases spread across a population. In many cases, these processes are strongly influenced by the large-scale organization of nodes into groups. As such, the algorithmic detection of tightly-connected groups of nodes, known as communities, has become a prominent method for studying various networks across different disciplines. Examples discussed in this talk include online social networks, political data, and features of pathogenic E.coli. No previous knowledge about networks will be assumed.
Bio: Peter Mucha is a Professor of Mathematics and Applied Physical Sciences at the University of North Carolina at Chapel Hill. Born in Texas and raised in Minnesota, Dr. Mucha moved east to attend college at Cornell University where he majored in Engineering Physics. After a Churchill Scholarship studying in the Cavendish Laboratory at Cambridge with an M.Phil. in Physics, he returned to the States to continue his studies at Princeton with an M.A. and Ph.D. in Applied and Computational Mathematics. Following a postdoctoral instructorship in applied mathematics at MIT, and a tenure-track assistant professorship in Mathematics at Georgia Tech, he moved to UNC-Chapel Hill, where he has served as chair of the Department of Mathematics, the founding chair of the Department of Applied Physical Sciences, and is the current Director of the Chairs Leadership Program at the Institute for the Arts & Humanities. His awards include a DOE Early Career PI award and NSF CAREER award. At UNC, he was recognized with a Bowman and Gordon Gray Distinguished Term Professorship for excellence in undergraduate teaching and he was named to the inaugural cohort of the Outstanding Postdoc Mentor Award. Dr. Mucha’s research includes a variety of topics in the mathematics of networks, including network representations of data, community detection, and modeling dynamics on and of networks. His group’s activities are fundamentally interdisciplinary, applying tools of network analysis and data science in collaborations across the mathematical, physical, life, and social sciences.