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Georgios B. Giannakis (born February 27, 1958) is a Greek–American Professor, engineer, and inventor. At present he is Professor in Wireless Telecommunications, and Director of the Digital Technology Center at the University of Minnesota. Giannakis is known for his work in the areas of statistical signal processing and wireless communications on topics such as auto regressive moving average system identification using higher order statistics, principle component filterbanks, linear precoding, multicarrier modulation, ultrawideband communication, cognitive radio, and smart grids. Seminal work includes the development of linear precoding wireless communication systems, which provided a unified approach for designing space-time block codes that achieve data high rates and reliability,  and proposal of zero-padding as an alternative to the cyclic prefix for multi-carrier communication systems , which had impact in the multi-band ultra wideband standard XXX REF XXX. Giannakis has left a substantial academic legacy as an advisor of more than forty Ph.D. dissertations at The University of Virginia and The University of Minnesota.

Early Life
Born in Corfu, Greece, Giannakis received his MA in Electrical Engineering from the National Technical University of Athens in 1981, his M.Sc. in Electrical Engineering from the University of Southern California in 1983, his M.Sc. in Mathematics from the University of Southern California in 1986, and his PhD in Electrical Engineering from the University of Southern California also in 1986. . After completing his Ph.D., he started his academic career at the University of Virginia in 1987 and moved to the University of Minnesota in 1999. As a professor, he built a distinguished research group making contributions in many areas including statistical signal processing, wireless communications, and data analytics.

System Identification Using Higher Order Statistics
Giannakis established an important result in the identification of a linear system based only on its output. He showed that non-minimum phase and non-causal parametric auto-regressive moving average models