I am a Ph.D. student at KAIST AI, advised by Prof. Edward Choi. My research focuses on large language models (LLMs) and multimodal learning, particularly in healthcare applications. I am interested in building and evaluating AI systems, from curating high-quality datasets to developing models that work reliably in real-world settings. Most recently, I have been exploring how LLMs can enable more realistic and grounded clinical interactions. As part of this effort, I developed PatientSim, a persona-driven framework for simulating realistic doctor-patient conversations grounded in real medical records.
* denotes equal contribution
Research Intern · Seoul, Korea
Developed a multimodal image-text framework for chest X-ray/report alignment, introducing a fine-tuning strategy with random sentence sampling and loss relaxation to improve zero-shot pathology classification. Achieved a 4.3% average increase in macro AUROC across four datasets, surpassing board-certified radiologists. Published in Scientific Reports.
Undergraduate Research Intern · Advisor: Jonghyun Choi
Conducted research on 3D point cloud domain adaptation. Proposed a search-based transformation approach for unsupervised domain adaptation, published in IEEE Access (2022).
Ph.D. in Artificial Intelligence (Integrated Master's/Doctoral)
Kim Jaechul Graduate School of AI · Advisor: Prof. Edward Choi
B.S. in Electrical Engineering and Computer Science
Gwangju Institute of Science and Technology
ICASSP 2023
GIST · Nov 2020
Reviewer: ICLR (2025–2026), ICASSP (2023–2026), ML4H (2024–2025), CHIL (2024–2025), COLM (2025), ARR (2025 Feb), NeurIPS Datasets & Benchmarks (2024)
Teaching: KoSAIM Summer School Hands-on Session — Numpy and Pytorch Intro (Aug 2023)
I'm always happy to discuss research collaborations, healthcare AI, or anything in between.