Biography

What should an AI system do when the world has changed? This question has shaped my research and my sense of purpose as a computer scientist, since I began my research at Korea University. I studied how AI models, especially language models, handle time-sensitive knowledge, why they become unreliable when facts or regulations change after the training cutoff, and how we can make them respond more responsibly. In my previous work, I developed benchmarks for temporal knowledge and explored how temporal knowledge is represented inside models through mechanistic interpretability, leading me to propose novel alignment method. What distinguishes my research is that I do not treat evaluation, interpretability, and training regimes as separate problems; I aim to connect them into a single framework for building trustworthy AI. In my PhD, I hope to devise time-aware AI agents that can recognize when their knowledge is outdated, retrieve fresh evidence, revise their beliefs, and remain well-calibrated under uncertainty. My long-term goal is to help build AI systems that are not only more capable, but more reliable, transparent, and genuinely useful in improving people’s lives.

Previous Education

Korea University AI Applications 2026
Korea University Linguistics, CSE 2024