Intro

I'm a Research Scientist at the Meta GenAI. Prior to joining Meta GenAI, I was a Postdoctoral Researcher at the Meta AI Research (FAIR), worked with Dr. Yuandong Tian and received my Ph.D. degree from Korea Advanced Institute of Science and Technology (KAIST) in 2023, advised by Prof. Sung Ju Hwang, and won the best Ph.D. Dissertation Award from the KAIST College of Engineering and School of Computing. During my Ph.D. study, I was a research intern at Meta AI and my research was supported by Google Ph.D. Fellowship.

My research focuses on developing efficient large-scale AI models for real-world applications. I specialize in model optimization techniques, including AutoML, Neural Architecture Search (NAS), meta-learning, hyperparameter optimization, and model compression through Knowledge Distillation (KD). Recently, I have been particularly interested in designing, analyzing, and evaluating large language models (LLMs) for efficient long-context generation and optimizing Retrieval-Augmented Generation (RAG) techniques, enabling practical deployment in real products.

Related keywords: LLM Efficiency, RAG, AutoML, NAS, KD, Meta-learning