Vasileios Moustakas
National Technical University of Athens
Short Bio
Vasileios Moustakas (Student Member, IEEE) received his integrated M.Eng. degree in Electrical and Computer Engineering from the University of Thessaly (UTH), Greece. His master’s thesis, titled “Dual-Mode Fractional Charge-Pump–Based DC–DC Converter”, focused on advanced power-management circuit design. He is currently pursuing a Ph.D. under the supervision of Professor Paul P. Sotiriadis at the National Technical University of Athens (NTUA) and serves as an academic fellow at the Archimedes Research Unit of the Athena Research Center, Greece.
He is the recipient of the Best Student Paper Award at the 14th International Conference on Modern Circuits and Systems Technologies (MOCAST 2025) and the Best Student Paper Award at the 3rd International Conference on Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications (FAIEMA 2025), and has co-authored additional award-winning papers.
Vasileios has gained substantial research and R&D experience through an internship at Indeex, where he contributed to the design of a MEMS viscosity sensor. He has also been an active member of the Centaurus Racing Team, designing PCBs for a Formula Student combustion vehicle and conducting initial R&D for the team’s first driverless platform.
Research Interests
His research interests include analog and mixed-signal microelectronic circuits, ultra-low-power design, and switched-capacitor architectures. His current work centers on analog neuromorphic computing and integrated-circuit architectures for machine-learning applications.
PhD research on "Design of analog and mixed-signal circuits for Machine Learning and Artificial Intelligence"
Abstract: Design of analog and mixed-signal circuits for Machine Learning focuses on creating energyefficient, high-speed hardware that integrates the analog domain. Our research develops circuits enabling low-power, high-performance computation, supporting real-time learning and inference in AI applications, particularly for edge devices requiring compact, scalable, and intelligent hardware solutions.
