Research Interests
I develop programming language techniques for addressing challenges across computer science,
in particular software engineering and machine learning. My approach is to design
domain-specific languages (DSLs) that express solutions to a target problem and to develop
program synthesis algorithms that automatically discover those solutions in the DSLs.
Current focus areas are DSLs and synthesis algorithms for effective pointer analysis — a key
component of compiler optimization and bug detection — for explainable graph machine learning,
and for identifying effective test cases in system software testing.
Static Program Analysis
Pointer Analysis
Program Synthesis
Domain-Specific Languages
Explainable Graph Learning
Fault Localization
Software Testing
7Top-tier PL papers
10Publications
4PC memberships
14+Invited talks
Employment
Sep. 2025 – Present
Assistant Professor, DGIST
— Department of Computer Science and Engineering
Jul. 2024 – Aug. 2025
Research Professor, Korea University
Mar. 2023 – Jun. 2024
Postdoctoral Researcher, Korea University
Education
Mar. 2017 – Feb. 2023
Integrated M.S. & Ph.D., Computer Science and Engineering, Korea University
Mar. 2011 – Feb. 2017
B.S., Computer Science and Engineering, Korea University
Research Grants
- Programming Language Technology for Explainable Graph Machine Learning
(설명 가능한 그래프 기계학습 방법 개발을 위한 프로그래밍 언어 기술 연구) — Principal Investigator
Service & Teaching
Program Committee
- NSAD 2026 — ACM SIGPLAN Int'l Workshop on Numerical and Symbolic Abstract Domains
- ICFP 2025 — ACM SIGPLAN Int'l Conference on Functional Programming
- SOAP 2025 — ACM SIGPLAN Int'l Workshop on the State Of the Art in Program Analysis
- OOPSLA 2024 — ACM Conf. on Object-Oriented Programming, Systems, Languages, and Applications
Journal Reviewing
- TOSEM (2025, 2026) — ACM Transactions on Software Engineering and Methodology
Teaching
- 2025 Fall — Program Analysis (DGIST IC637)
- 2024 Fall — Data Structures (Korea University COSE214)
Publications
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KDD 26
Minseok Jeon, Seunghyun Park, and Jun-Gi Jang.
ProgNet: Program-Grounded Evidence Composition for Interpretable Graph Classification.
32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2026 (accepted).
-
OOPSLA 25
Donguk Kim, Minseok Jeon*, Doha Hwang, and Hakjoo Oh* (*corresponding authors).
PAFL: Enhancing Fault Localizers by Leveraging Project-Specific Fault Patterns.
ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications, Oct. 2025.
-
PLDI 24
Minseok Jeon, Jihyeok Park, and Hakjoo Oh.
PL4XGL: A Programming Language Approach to Explainable Graph Learning.
ACM SIGPLAN Conference on Programming Language Design and Implementation, Jun. 2024.
-
ICST 23
Jinkook Kim, Minseok Jeon, Sejeong Jang, and Hakjoo Oh.
Automating Endurance Test for Flash-based Storage Devices in Samsung Electronics.
IEEE International Conference on Software Testing, Verification and Validation (Industry Track), Apr. 2023.
-
POPL 22
Minseok Jeon and Hakjoo Oh.
Return of CFA: Call-Site Sensitivity Can Be Superior to Object Sensitivity Even for Object-Oriented Programs.
49th ACM SIGPLAN Symposium on Principles of Programming Languages, Jan. 2022.
-
IST 21
Donghoon Jeon, Minseok Jeon, and Hakjoo Oh.
A Practical Algorithm for Learning Disjunctive Abstraction Heuristics in Static Program Analysis.
Information and Software Technology, Vol. 135, Jul. 2021.
-
OOPSLA 20
Minseok Jeon, Myungho Lee, and Hakjoo Oh.
Learning Graph-based Heuristics for Pointer Analysis without Handcrafting Application-Specific Features.
ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications, Nov. 2020.
-
TOPLAS 19
Minseok Jeon*, Sehun Jeong*, Sungdeok Cha, and Hakjoo Oh (*co-first authors).
A Machine-Learning Algorithm with Disjunctive Model for Data-Driven Program Analysis.
ACM Transactions on Programming Languages and Systems, Jun. 2019.
-
OOPSLA 18
Minseok Jeon, Sehun Jeong, and Hakjoo Oh.
Precise and Scalable Points-to Analysis via Data-Driven Context Tunneling.
ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications, Nov. 2018.
-
OOPSLA 17
Sehun Jeong*, Minseok Jeon*, Sungdeok Cha, and Hakjoo Oh (*co-first authors).
Data-Driven Context-Sensitivity for Points-to Analysis.
ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications, Oct. 2017.
Selected Talks
Nov. 2025
AI를 활용한 수업자료 자동 생성 프레임워크 — AI 활용 경진대회, DGIST
Oct. 2025
Developing Cost-Effective Combinations of Static Analysis Techniques
— Dagstuhl Seminar 25421, Germany
Aug. 2025
컨텍스트 터널링: 고정관념에 도전하기 — SIGPL Summer School, Sogang University
Jul. 2025
성공적인 연구를 위한 문제 발견하기 — Software Analysis Lab Seminar, Korea University
Aug. 2024
될 때까지 개선하기 — SIGPL Summer School, Sungkyunkwan University
Jun. 2024
PL4XGL: A Programming Language Approach to Explainable Graph Learning
— PLDI 2024, Copenhagen, Denmark
May 2024
PL4XGL: 프로그래밍 언어 기법을 활용한 설명 가능한 그래프 기계학습 방법
— ProSysLab Seminar, KAIST
Jan. 2024
그래프 패턴 언어를 활용하여 다양한 분야의 핵심 문제 접근하기 — STAAR Workshop, KAIST
Nov. 2023
Data-Driven Static Analysis — CSE GSAI Seminar, POSTECH
Jan. – Feb. 2022
Return of CFA: Call-Site Sensitivity Can Be Superior to Object Sensitivity
— POPL 2022, Philadelphia, USA; STAAR Workshop, Jeju
Nov. 2020
Learning Graph-based Heuristics for Pointer Analysis
— OOPSLA 2020, Online; KSC 2020
Nov. 2018
Precise and Scalable Points-to Analysis via Data-Driven Context Tunneling
— OOPSLA 2018, Boston, USA
Jan. – Jun. 2018
Data-Driven Context-Sensitivity for Points-to Analysis
— KCSE 2018, Pyeongchang; KCC 2018, Jeju
Research Group — PLX Lab @ DGIST
PLX Lab develops programming language technologies for problems in other computer science domains,
along two directions: PL4SE (PL for software engineering) and PL4ML (PL for machine
learning). The lab is recruiting students interested in program analysis, synthesis, and language design.
— https://dgistpl.github.io
