Junghyun Lee is a third-year PhD student at Kim Jaechul Graduate School of AI, KAIST, jointly advised by Professors Se-Young Yun (OSI Lab) and Chulhee "Charlie" Yun (OptiML Lab). His research spans mathematical and theoretical AI, recently focusing on interactive machine learning and the statistical analysis of large networks, with an emphasis on community detection. In particular, he has been looking into problems related to reinforcement learning with human feedback (RLHF) and preference learning. His work has been presented at leading international AI conferences, such as AISTATS, NeurIPS, ICLR, and AAAI, where he has also contributed as a reviewer. He received the Best Student Paper Award at OPODIS 2023 and Best Paper Awards at multiple Korean AI conferences, including KSC '23, JKAIA '21, and CKAIA '22 and '24. In the summer of 2025, he will join Adobe Research, San Jose, as a research intern, working with Branislav "Brano" Kveton. Junghyun earned his MSc from the same school and his BSc in Mathematical Sciences and Computer Science (double major, cum laude), also from KAIST.