October 6, 2017, Georgios B. Giannakis, University of Minnesota

Friday, October 6, 2017 at 3:00 PM to 4:00 PM
8 St. Mary’s Street, PHO 211
Refreshments at 2:45pm



Georgios B. Giannakis
University of Minnesota

Learning Nonlinear and Dynamic Network Connectivity and Processes on Graphs

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 University of Virginia from 1987 to 1998, and since 1999 he has been a professor with the Univ. of Minnesota, where he holds a Chair in Wireless Telecommunications, 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 127). Current research focuses on big data analytics, wireless cognitive radios, network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 30 patents issued, and the (co-) recipient of 8 best paper awards from the IEEE Signal Processing (SP) and Communications Societies, including the G. Marconi Prize Paper Award in Wireless Communications. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), a Young Faculty Teaching Award, the G. W. Taylor Award for Distinguished Research from the University of Minnesota, and the inaugural IEEE Fourier Technical Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in a number of posts including that of a Distinguished Lecturer for the IEEE-SP Society.

Faculty Host: Yannis Paschalidis
Student Host: Tingting Xu