Date: February 14, 2020
Location: CSB 480
Speaker: Prof. Victor Preciado
Abstract: Using methods from algebraic graph theory and convex optimization we study the relationship between local structural features of a network and global spectral properties. In particular, we derive expressions for the so-called spectral moments of a graph in terms of local structural measurements, such as subgraph densities. Furthermore, we propose a series of semidefinite programs to compute bounds on the spectral radius, and other spectral properties, from a truncated sequence of spectral moments. Using our tools, we illustrate how important spectral properties of real-world networks are strongly constrained by local structural features.
Bio: Victor M. Preciado received the PhD degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and is currently an Associate Professor of Electrical and Systems Engineering at the University of Pennsylvania, where he is a member of the Networked and Social Systems Engineering (NETS) program, the Warren Center for Network and Data Sciences, and the Applied Math and Computational Science (AMCS) program. He was a recipient of the 2017 National Science Foundation CAREER Award, the 2018 Outstanding Paper Award by the IEEE Control Systems Magazine, and a runner-up of the 2019 Best Paper Award by the IEEE Transactions on Network Science and Engineering. His main research interests lie at the intersection of big data and network science; in particular, in using innovative mathematical and computational approaches to capture the essence of complex, high-dimensional dynamical systems. Relevant applications of this line of research can be found in the context of socio-technical networks, brain dynamical networks, healthcare operations, biological systems, and critical technological infrastructure. He is a Senior Member of IEEE and Associate Editor of the IEEE Transactions on Network Science and Engineering.