Sang Kil Cha

Sang Kil Cha is a Faculty member at the School of Computing, Korea Advanced Institute of Science and Technology (KAIST). He is associated with the Cyber Security Research Center (CSRC) and leads the SoftSec Lab at KAIST. His research focuses on computer security and software engineering, particularly on building and evaluating systems for program analysis. He teaches courses on binary code analysis, secure software systems, and information security. His work includes publications in top conferences and journals, with a focus on topics such as fuzzing, binary analysis, and software security.

Sang Kil Cha is a researcher in the fields of Security, Software Engineering, and Program Analysis. His work focuses on improving software security through advanced fuzzing techniques, exploit generation, and binary code analysis. He has contributed to the development of tools and methodologies for enhancing fuzzing efficiency, such as Program-adaptive Mutational Fuzzing and Symbolic Execution with Veritesting. His research also explores the application of machine learning and neural networks in fuzzing, as seen in Montage and IMF. Cha's publications highlight the importance of static and dynamic data-flow analyses in smart contract and JavaScript engine vulnerability detection.