Junghyun Lee is a second-year PhD student at the Kim Jaechul Graduate School of AI at KAIST, where he is jointly advised by Professors Se-Young Yun and Chulhee Yun. His broad research interests lie in mathematical and theoretical AI, recently focusing on statistical challenges in Reinforcement Learning from Human Feedback (RLHF). This includes interactive machine learning domains like bandits, reinforcement learning, online learning, and active learning, as well as low-dimensional structure recovery techniques such as clustering. Also being passionate about fundamental concepts in statistics and probability theory, algorithmic fairness, and deep learning theory, Junghyun is intrigued by any machine learning challenge that necessitates rigorous mathematical analysis. His research has been featured at prestigious international AI conferences, including AISTATS, NeurIPS, ICLR, and AAAI, where he has also served as a reviewer. He received the Best Student Paper Award at OPODIS 2023 and the Best Paper Award at several domestic(Korean) AI conferences, including JKAIA '21 and CKAIA '22 and '24. Junghyun holds an MSc from the same school and a BSc in Mathematical Sciences and Computer Science (double major, cum laude) from KAIST.