Prof. Georgios Giannakis, University of Minnesota, to speak at the NTUA on Tuesday, July 10, 2018 at 16:00, Central Library of NTUA (Multimedia Amphitheater)
The event is organized by the Division of Signals, Control and Robotics of ECE NTUA and the IEEE Signal Processing Society Greek Chapter
Lecture Title: "Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics"
Learning the topology of graphs as well as processes evolving over graphs are tasks emerging in application domains as diverse as gene-regulatory, brain, power, and social networks, to name a few. Scalable approaches to deal with such high-dimensional settings aim to address the unique modeling and computational challenges associated with data-driven science in the modern era of big data analytics. Albeit simple and tractable, linear time-invariant models are limited as they are incapable of modeling changing topologies, as well as nonlinear and dynamic dependencies between nodal processes. To this end, novel approaches are presented to leverage nonlinear counterparts of partial correlation and partial Granger causality, as well as nonlinear structural equations and vector auto-regressions, along with attributes such as low rank, sparsity, and smoothness to capture even directional dependencies with abrupt change points, as well as dynamic processes over possibly time-evolving topologies. The unifying framework inherits the versatility and generality of kernel-based methods, and lends itself to batch and computationally affordable online learning algorithms, which include novel Kalman filters and smoothers over graphs. Real data experiments highlight the impact of the nonlinear and dynamic models on gene-regulatory and functional connectivity of brain networks, where connectivity patterns revealed exhibit discernible differences relative to existing approaches**.**
Georgios B. Giannakis (Fellow’97) received his Diploma in Electrical Engr. from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the U. of Virginia from 1987 to 1998, and since 1999 he has been a professor with the U. of Minnesota, where he holds a Chair in Wireless Communications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 400 journal papers, 700 conference papers, 25 book chapters, two edited books and two research monographs (h-index 131). Current research focuses on data science and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 32 patents issued, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), and the inaugural IEEE Fourier Tech. Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in various posts including that of a Distinguished Lecturer.
For more information please contact : Prof. P. Maragos (email: email@example.com)