Georgios Giannakis

Georgios Giannakis is a Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research spans communications, networking, signal processing, and machine learning with applications to wireless systems and networks.

Professor Giannakis' research interests include:

• Signal Processing and Analysis
• Wireless Communications and Networking
• Machine Learning for Signal Processing
• Graph Signal Processing
• Bayesian Optimization and Inference
• Federated and Distributed Learning

His current research focuses on complex-field and network coding, cooperative wireless communications, cognitive radios, cross-layer designs, mobile ad hoc networks, and wireless sensor networks. Recent work has expanded into graph learning, Bayesian optimization, and federated learning approaches for communication systems with applications to 5G/6G networks, Internet of Things, and wireless sensor networks.

Professor Giannakis has received significant research funding, including multiple NSF grants such as "Resonant-Beam based Optical-Wireless Communication," "Robust Learning over Graphs," "Learning-driven Models for 5G Internet Measurements," and "Online Learning for IoT Monitoring and Management." His fingerprint analysis reveals key research areas including fading channels (81%), sparsity (71%), multiuser systems (66%), wireless sensor networks (56%), and transmitter technologies.

His scientific contributions include over 1,188 research outputs with consistent productivity across decades, demonstrating his sustained impact in the field. His recent publications show a clear trend toward integrating machine learning techniques with traditional signal processing for next-generation communication systems that require robustness, efficiency, and adaptability in dynamic environments.