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• Programming Languages
• Program Analysis
• JavaScript
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Sukyoung Ryu is a Professor and Head of the School of Computing at Korea Advanced Institute of Science and Technology (KAIST). She leads one of South Korea's premier computing education and research institutions. Her academic foundation was built entirely at KAIST where she earned: PhD in Computer Science (1996-2001) MS in Computer Science (1995-1996) BS in Computer Science (1991-1995) Dr. Ryu's research focuses on developing advanced program analysis techniques with particular emphasis on JavaScript through her SAFE framework. She also works on enhancing medical image processing performance, creating practical program analysis tools that balance accuracy with scalability, and developing programming environments for debugging and testing. Her work extends to proof assistants for programming languages and program analyses, bridging theoretical foundations with practical applications. Prior to her current position, Dr. Ryu gained valuable experience as a Research Associate at Harvard University (2001-2005) and as a Member of Technical Staff at Sun Microsystems Laboratories (2005-2009). As Head of School, Dr. Ryu oversees academic programs, faculty development, research initiatives, and strategic planning for KAIST's computing department. Her leadership places her at the forefront of shaping computer science education and research direction in South Korea.

• Program Analysis
• Program Synthesis and Transformation
• Machine Learning for Programming Systems
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Kihong Heo is an Associate Professor at the School of Computing and Graduate School of Information Security, KAIST, Republic of Korea. He leads the Programming Systems Laboratory and has held academic positions since 2017, including Assistant Professor (2017-2020) and Associate Professor (2020-2024). His research focuses on developing program reasoning systems for safe and reliable software, with key contributions in AI-based program analysis, program simplification, and scalable synthesis systems. His educational background includes a Ph.D. and B.S. in Computer Science & Engineering from Seoul National University (2009 and 2005 respectively). He has served as a post-doctoral researcher at the University of Pennsylvania (2009-2017) before transitioning to KAIST. Research interests span AI-driven program analysis for deep semantic bug detection, program debloating systems for security optimization, and synthesis frameworks using probabilistic models. His work bridges undecidability in static analysis with external information integration. Recent publications analyze logic-based verification for mobile agents, compiler fuzzing , and data dependency guided fuzzing . Trends show a focus on security-critical systems, reinforcement learning for code reduction, and probabilistic models. Scientific awards include the ACM SIGSOFT Distinguished Paper Award (FSE 2025) , Amazon Research Award (2024) , and multiple KAIST teaching honors . He has also received ACM SIGPLAN Distinguished Paper (PLDI 2019) and Best Artifact Awards. He has mentored numerous Ph.D. and Master’s students and contributes to program committees for leading conferences like SOAP (PC Co-Chair) , ICSE , and SAS . His work is supported by grants and collaborations with institutions including Facebook and Seoul National University.