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Al Bovik has research interests that span the disciplines of digital video, visual neuroscience, and deep learning. His is particularly interested in developing now theories of visual perception that can be translated into practical systems for obtaining the highest quality streaming video and social media and virtual and augmented reality. For his work in these areas, he received the IEEE Edison Medal, IEEE Fourier Award, Primetime Emmy Award for Outstanding Achievement in Engineering Development from the Television Academy, Technology and Engineering Emmy Award from the National Academy for Television Arts and Sciences, Progress Medal from The Royal Photographic Society, Edwin H. Land Medal from Optica, and the Norbert Wiener Society Award and Karl Friedrich Gauss Education Award from the IEEE Signal Processing Society. He has also received about 10 ‘best journal paper’ awards. He is an elected member of the United States National Academy of Engineering, the Indian National Academy of Engineering, the National Academy of Inventors, and Academy Europaea. His books include The Essential Guides to Image and Video Processing. He co-founded and was the longest-serving Editor-in-Chief of the IEEE Transactions on Image Processing and created/Chaired the IEEE International Conference on Image Processing which was first held in Austin, Texas, 1994.

We develop theory and invent technologies for intelligent wireless communications. Recent research interests:

• Machine Learning for Intelligent Communications
• Optimization of (massive) MIMO interference networks;
• Information theory and coding;
• Synchronization algorithms;
• Machine learning aided wireless communication;
• Software defined radio (SDR) to transfer our research to real world systems;
• Characterization of random networks using stochastic geometry;
• Performance analysis of communication systems with feedback.

Peter Mathys and his group work at the intersection of communication and coding theory and practical implementations using software-defined radio (SDR), digital signal processing (DSP), and machine learning (ML). Of particular interest is the combination of SDRs and ML to create intelligent radio networks that are capable of sharing the radio frequency (RF) spectrum, which is a finite natural resource, in novel and more efficient ways. Another more interdisciplinary application of wireless communications, SDR and DSP for medical purposes is the design and implementation of passive implants that are powered by RF energy while at the same time receiving and transmitting bio-sensing data wirelessly.

Mahesh Varanasi and his group work in the areas of information theory, wireless communications and detection/estimation theory. Their focus at this time is on developing fundamental understanding of, and methods for, efficient and reliable transmission of data over networks. Of particular interest are networks that incorporate protocols to enable advanced features such as multiple-antenna terminals, cooperation and relays, cognition, spectrum sensing, simultaneous multiple groupcasting, security, privacy and feedback. Network topologies include small-to-large-scale single-cell, multi-cell, interference and relay networks, multihop-multiflow networks and cache networks. Information theoretic limits of such networks as well as the development of optimal and near-optimal methods are of interest as is the study of combinatorial and other structure of solutions and algorithms for their efficient computation.