Hong Jin Kang

Hong Jin Kang is a Lecturer in the School of Computer Science at The University of Sydney. His research area lies in Active Learning and AI for Software Engineering, with an overarching aim of improving developer productivity. His work focuses on the application of human-centric AI to a range of software engineering tasks, including secure code development. Prior to this role, he conducted research at leading software engineering groups as a postdoctoral fellow at UCLA and a Ph.D. student at Singapore Management University. His industrial collaborations have led to the deployment of research techniques in industry, with practical impact in discovering vulnerabilities leading to assigned CVEs. His work has been published at top venues of Software Engineering research, including ICSE, FSE, ASE, TSE, and TOSEM. He has also served on the program committees and as a reviewer at the top publication venues.

Hong Jin Kang is a researcher in Software Engineering with a focus on Active Learning. Their work explores methods to enhance the efficiency and reliability of software systems through automated techniques. Kang's research includes developing tools for detecting vulnerabilities, improving code quality, and enabling controlled testing environments. They have contributed to projects such as Bugsinpy, a database for Python bug studies, and Vulcurator, a vulnerability-fixing commit detector. Additionally, Kang has explored the use of machine learning for code analysis, including the compression of pre-trained models and the assessment of code embeddings. Their research emphasizes practical applications in software development and testing.