G. B. Giannakis (IEEE 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. Since 1999 he has been a professor with the Univ. of Minnesota, where he now holds an ADC Chair in Wireless Telecommunications in the ECE Department, 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 350 journal papers, 570 conference papers, 20 book chapters, two edited books and two research monographs (h-index 101). Current research focuses on sparsity in signals and systems, wireless cognitive radios, mobile ad hoc networks, wireless sensor, renewable energy, power grid, gene-regulatory, and social networks. He is the (co-) inventor of 21 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, and the G. W. Taylor Award for Distinguished Research from the University of Minnesota. 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.
My SOBACO-related interests are centered around big data challenges emerging with online social media, social data analytics, and social networks. Specific areas include visualization, modeling, inference, dimensionality reduction, clustering, reconstruction, anomaly detection, topology identification, and tracking of dynamic social networks. The tools and novel approaches lie at the intersection of machine learning theory and performance evaluation of big data algorithms, compressive sampling, collaborative filtering, robust statistics, network science, distributed, and online optimization techniques.
B. Baingana and G. B. Giannakis, “Embedding Graphs under Centrality Constraints for Network Visualization," IEEE Trans. on Knowledge and Data Engineering, submitted Feb. 2013.
K. Slavakis, G. Leus, and G. B. Giannakis, "Online Robust Portfolio Risk Management using Total Least-Squares and Parallel Splitting Algorithms ," Proc. of Intl. Conf. on Acoust., Speech, and Signal Processing, Vancouver, Canada, May 26-31, 2013.
M. Mardani, G. Mateos, and G. B. Giannakis, “Dynamic Anomalography: Tracking Network Anomalies via Sparsity and Low Rank," IEEE Journal of Sel. Topics in Signal Processing, vol. 7, no. 1, pp. 50-66, February 2013.
K. Slavakis, G. B. Giannakis, and G. Leus, “Nonlinear Compression and Reconstruction via Robust Sparse Embeddings," Proc. of Conf. of Info. Sciences, and Systems, Johns Hopkins Univ., Baltimore, March 20-22, 2013.
P. Forero, A. Cano, and G. B. Giannakis, “Consensus-Based Distributed Support Vector Machines," Journal of Machine Learning Research, vol. 11, pp. 1663-1707, May 2010.