Taejoon Byun

Papers

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About

Taejoon Byun is a researcher at the forefront of safety-critical artificial intelligence, with a primary focus on the verification and validation of deep neural networks (DNNs). His most cited work, "Input Prioritization for Testing Neural Networks" (2019), addresses a critical challenge in deploying DNNs for high-stakes domains like autonomous driving and medical diagnostics: how to efficiently test these complex systems to uncover failures before they lead to catastrophic outcomes. Byun’s contributions center on developing systematic testing methodologies that prioritize inputs most likely to reveal defects, thereby enhancing the reliability and robustness of AI systems. This work, which has garnered 6 citations, is foundational for researchers and engineers working to bridge the gap between AI’s theoretical promise and its safe real-world deployment. His research is particularly impactful in the context of self-driving cars, autonomous air vehicles, and industrial robotics, where system failures can result in loss of life or property. Byun’s achievements underscore his commitment to ensuring that as AI becomes more integrated into critical infrastructure, it does so with rigorous, verifiable safety guarantees.