Masaya Yoshikawa

Papers

Total Citations

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About

Masaya Yoshikawa is a leading researcher at the intersection of hardware security, artificial intelligence, and embedded systems. His work addresses critical vulnerabilities in modern computing, particularly focusing on hardware Trojans (HTs) in FPGA-based AI modules—a key concern for automotive and robotic systems where edge AI is deployed. His 2020 paper on this topic, which has garnered 8 citations, highlights novel attack vectors that threaten the integrity of AI hardware. Yoshikawa also made early contributions to reinforcement learning, proposing a Q-learning algorithm enhanced by a hierarchical evolutionary mechanism (2008, 8 citations), blending genetic algorithms with adaptive control for robotics. More recently, he has evaluated authenticated encryption schemes like CAESAR for secure robot operating systems (SROS2), addressing cyber-security challenges in Industry 4.0 factory automation. With a research portfolio that spans from foundational reinforcement learning to cutting-edge hardware security, Yoshikawa’s work is essential for students and engineers developing secure, intelligent autonomous systems. His contributions are shaping the future of trustworthy AI deployment in safety-critical environments.