Ernest Bonnah

Ernest Bonnah is an Assistant Professor in the Department of Electrical and Computer Engineering at Baylor University, Waco, Texas. He leads the Secured and Dependable Intelligent Systems (SeDIS) Laboratory, conducting cutting-edge research in formal verification, security, and reliability of cyber-physical and autonomous systems.

Education:

• Ph.D., Electrical and Computer Engineering, University of Missouri-Columbia
• M.S., Computer Science, University of Missouri-Columbia
• M.S., Telecommunications Engineering, Kwame Nkrumah University of Science and Technology (KNUST), Ghana
• B.Sc., Computer Engineering, Kwame Nkrumah University of Science and Technology (KNUST), Ghana

His research lies at the intersection of formal methods, machine learning, and cybersecurity, with a focus on ensuring safety and trustworthiness in intelligent autonomous systems. He investigates techniques for formal verification of cyber-physical systems, explainable AI for robotic failures, and security in Internet of Autonomous Things (IoAT) and edge networks. His application domains include robotics, edge computing, and safety-critical health systems.

The recent publication trends reflect a strong emphasis on formal verification using SMT-based model checking, particularly for temporal logic specifications in complex systems. His work integrates theoretical rigor with practical implementations to address real-world security and reliability challenges.

Scientific Awards:

Dr. Bonnah actively mentors graduate students and leads a funded research lab (SeDIS) at the Baylor Research and Innovation Collaborative (BRIC). He is currently recruiting Ph.D. students for fully funded positions, indicating active grant support and research supervision. His team explores novel security, blockchain, and machine learning techniques to enhance quality of service and safety in intelligent systems.

The SeDIS Laboratory conducts interdisciplinary research in formal verification, explainable AI, and security for autonomous and cyber-physical systems. Located in the BRIC building, the lab provides a collaborative environment for advancing dependable intelligent computing.