Rate My Professor Brook Abegaz

Brook Abegaz, PhD, is an Associate Professor of Engineering at Loyola University Chicago, specializing in computer engineering. He earned a Master of Science in Computer Engineering from Delft University of Technology in the Netherlands in 2012 and a PhD in Electrical and Computer Engineering from Tennessee Technological University in 2016. Before joining Loyola University Chicago, Dr. Abegaz served as an instructor at Tennessee Technological University, where he taught courses including Physical Electronics and laboratory courses on Digital Systems, Computer-Aided Engineering, and Circuits I and II. He held research fellowships at TTU's Center for Energy Systems Research and Center for the Management, Utilization and Protection of Resources. Additionally, he gained industry experience at Intel Corporation in California, Qurrent Energy in Amsterdam, and the Netherlands Organization for Applied Scientific Research (TNO) in Delft, Netherlands. Dr. Abegaz has mentored undergraduate students on research projects such as improving the security of autonomous vehicles using supervised machine learning and designing predictive methods for obstacle detection and safe navigation.

Dr. Abegaz's research specializes in modern sensor technologies and expert systems to enhance the stability, reliability, resilience, and security of cyber-physical systems, including electronic, storage, and power systems. His work focuses on developing on-chip systems comprising processors, memory, and control units for spatio-temporal network analysis and perturbation tolerance in converter-connected power systems, tackling issues like power, voltage, frequency perturbations, dynamic sources, environmental variabilities, and node/link failures. He has produced 36 research outputs, including journal articles and conference proceedings. Key publications include "Optimal Perturbation Tolerance in VSC-Connected Hybrid Networks using an Expert System-on-a-Chip" (IEEE Transactions on Power Electronics, 2017), "Sensor Technologies for the Energy-Water Nexus – A Review" (Applied Energy, 2017), "Coordinated Motion Planning for Multi-Degrees of Freedom Industrial Robots using Edge Computing Devices" (2025), "Proactive Semi-Supervised Machine Learning Method for Stability Estimation in Smart Grids" (2024), "Design of a Convolutional Neural Network and a Modified Genetic Algorithm for Power Grid Disturbance Classification" (2024), "Smart Control of Buck Converters using a Switching-based Clustering Algorithm" (2019), "Smart Control of Automatic Voltage Regulators using K-means Clustering" (2019), and "A Parallelized Self-Driving Vehicle Controller using Unsupervised Machine Learning" (IEEE Transactions on Industry Applications, 2022).