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Many social, biological and technological systems take the form of complex networks. Examples include friendship and collaboration networks, neural networks, food webs, power grids, and the Internet. Understanding that these systems cannot be well represented by low-dimensional lattices of mean field approximations, and that the intricate non-homogenous tangles of interacting elements must be explicitly taken into account, can give us new insights into hard problems. My research combines methods from statistical mechanics, dynamical systems, and graph theory to address interdisciplinary, network-related problems. I am interested in both broad theoretical approaches to complex networks as well as specific applications, especially to information cascades, epidemiology, and genetic regulatory networks.
Professor Girvan received her B.S. in physics and B.S. in mathematics with a minor in political science from the Massachusetts Institute of Technology, Cambridge, MA, in June 1999. She received her Ph.D. in physics from Cornell University, Ithaca, NY, in August 2003.