Program analysis and time complexity prediction using large language models
Specification-driven test case generation and validation
Multi-agent debate frameworks for LLM-based reasoning and decision-making
Education
Integrated MS/PhD in Computer Science, Yonsei University, Seoul, Korea (2024–Present)
Research: time complexity prediction, test case generation, multi-agent debate for program analysis
Bachelor's degree in Information and Communication Engineering, Myongji University (2018–2024)
Experience
Theory of Computation Lab, Yonsei University
Graduate Researcher (2024–Present): Leading projects on time complexity prediction, test case generation, multi-agent debate for program analysis
Publications
TCProF: Time-Complexity Prediction SSL Framework Joonghyuk Hahn, Hyeseon Ahn, Jungin Kim, Soohan Lim, Yo-Sub Han NAACL 2025
A semi-supervised framework for predicting code time complexity using symbolic analysis in low-resource settings.
Professional Activities
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Skills
Programming: C/C++, Python
Tools/Frameworks: Git, PyTorch, TensorFlow, LLM
Advisor
Prof. Yo-Sub Han
Department of Computer Science, Yonsei University
Email: emmous@yonsei.ac.kr