Research on system modeling and quality assurance
Research is conducted on methods to model complex systems, verify the correctness of design and implementation, and provide a certain level of assurance on their quality. We aim to develop techniques that scale to practical use and have an impact, focusing on embedded systems and machine learning-based systems for cyber physical systems and IoT. We will also work on safety- and security-conscious design methods and anomaly detection methods that enable cyber attack detection.
Research on software analysis technology
Research is conducted on techniques to analyze software source code and binary code (executable programs) to enable identification and prediction of defects. For source code analysis, we will focus on techniques to analyze similarities and changes, and develop techniques to improve the reliability of large-scale systems by applying statistical machine learning methods for text processing. As for binary code analysis, we aim to develop tools to automatically discover unknown vulnerabilities.