I am broadly interested in developing programming language techniques for addressing challenges in various fields, including software engineering and machine learning. Specifically, I take pleasure in designing domain-specific programming languages (DSLs) and developing program synthesis algorithms to address these challenges.

• Pointer & static analysis — designing DSLs and synthesis algorithms for effective, precise, and scalable pointer analysis, a key component in compiler optimization.
• Explainable graph machine learning — programming language approaches to make graph-based machine learning models interpretable.
• System software testing — DSLs tailored to identifying effective test cases in system software testing.
• Program synthesis — algorithms for synthesizing programs and heuristics from data, applied throughout the above areas.

Research Grants

• 설명 가능한 그래프 기계학습 방법 개발을 위한 프로그래밍 언어 기술 연구 (Programming Language Technology for Explainable Graph Machine Learning)

Selected Talks

• AI를 활용한 수업자료 자동 생성 프레임워크. AI 활용 경진대회, DGIST. Nov. 4, 2025.
• Developing Cost-Effective Combinations of Static Analysis Techniques. Dagstuhl Seminar 25421 (Sound Static Program Analysis in Modern Software Engineering), Dagstuhl, Germany. Oct. 16, 2025.
• 컨텍스트 터널링: 고정관념에 도전하기. SIGPL Summer School, Sogang University. Aug. 21, 2025.
• 성공적인 연구를 위한 문제 발견하기. SAL Lab Seminar, Korea University. Jul. 18, 2025.
• 될 때까지 개선하기. SIGPL Summer School, Sungkyunkwan University. Aug. 23, 2024.
• PL4XGL: A Programming Language Approach to Explainable Graph Learning. Paper presentation at PLDI 2024, Copenhagen, Denmark. Jun. 27, 2024.
• PL4XGL: 프로그래밍 언어 기법을 활용한 설명 가능한 그래프 기계학습 방법. KAIST (ProSysLab Seminar). May 3, 2024.
• 그래프 패턴 언어를 활용하여 다양한 분야의 핵심 문제 접근하기. STAAR Workshop, KAIST. Jan. 30, 2024.
• Data-Driven Static Analysis. POSTECH, Pohang, Korea. Nov. 15, 2023.

See the Publications page for the full list of papers.