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Theocharis Theocharides

Prof. Theocharis (Theo) Theocharides holds a Ph.D. in Computer Science and Engineering from the Pennsylvania State University. He is an Associate Professor at the Department of Electrical and Computer Engineering, at the University of Cyprus and the Director of Research of the KIOS Research and Innovation Centre of Excellence. His research focuses on the broad area of intelligent embedded systems design, with emphasis on domain-specific architectures, evolvable and reconfigurable hardware, and design of reliable and low power embedded and application specific processors and circuits. He directs the Embedded and Application-Specific System-on-Chip Lab, which runs several projects related to embedded, mobile and reconfigurable systems, tinyML, embedded computer vision, embedded pattern recognition and classification architectures, and intelligent system-level monitoring and dynamic reconfiguration for performance, energy and reliability of Systems-on-Chip. He has authored/co-authored more than 130 papers and book chapters in internationally acclaimed books and peer-reviewed scientific journals and conferences. He is a senior member of the IEEE and the IEEE Computer Society, a member of the ACM, a member of the HiPEAC Network of Excellence. He is an Associate Editor for ACM’s Computing Surveys, ACM’s Transactions on Emerging Technologies for Computing Systems, for IEEE Transactions on Computer Aided Design, for IEEE Design and Test, for IEEE Consumer Electronics magazine, for the ETRI Journal, for IET’s Computers and Digital Techniques, and for Springer Nature’s Lecture Notes on Computer Science. He also serves on several Organizational and Technical Program Committee boards of various IEEE/ACM Conferences, including the roles of Track chair for Design, Automation and Test in Europe (DATE) from 2020-2022, and Design Automation Conference (2021-2022). Theocharis and his students have extensive and pioneering work on hardware acceleration for computer vision applications, including object detection, pattern recognition and depth estimation from stereoscopic video, yielding real-time, high frame-rates and low-power, suitable for emerging embedded vision systems, mobile robotics, and cyber-physical systems. His present work focuses on development of hardware-friendly machine learning algorithms for tinyML applications, visual information extraction, and distributed embedded computer vision applications. Further, he and his students investigate vision-based robotic collaboration for various monitoring and visual information extraction algorithms, involving smart camera networks, robot swarms, and autonomous aerial (drones) and terrestrial robots.