Assist. Prof. Ugur Yayan
Papers
1
Total Citations
2
H-Index
1
About
Dr. Ugur Yayan is an Assistant Professor whose research lies at the intersection of robotics, artificial intelligence, and safety-critical systems. His work focuses on developing robust verification and validation (V&V) methodologies for AI-driven autonomous platforms, with a particular emphasis on fault injection and anomaly detection. In his most-cited study, "Manipulation of Camera Sensor Data via Fault Injection for Anomaly Detection Studies in Verification and Validation Activities For AI" (2021, 2 citations), Dr. Yayan pioneered a systematic approach to creating deformed image databases by injecting faults directly into robot camera nodes. This work provides a crucial framework for stress-testing AI perception systems, enabling researchers to simulate real-world sensor degradation and evaluate system resilience before deployment. By bridging the gap between hardware-level faults and software-level anomaly detection, his contributions support the development of more trustworthy autonomous systems. Dr. Yayan’s research is particularly valuable for engineers and scientists working on safety assurance in robotics, where understanding how sensor failures impact AI decision-making is essential for certification and real-world deployment.
Research Focus
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Top Papers
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