I am an Machine Learning Masters student from the Kim Jaechul Graduate School of AI at KAIST. I am currently advised by Professor Jongchul Ye. I have a deep interest in pursuing a PhD in AI/ML studies, specializing in Diffusion models. Currently, my primary focus is on efficient training of Diffusion models, such as representation alignment. However, I also hold an interest in exploring discrete diffusion models on language domain.
Experiences
KAIST
Seoul, Korea (Aug 2025 -)
Masters
Kim Jaechul Graduate School of AI
Dalpha
Seoul, Korea (Aug 2023 -)
AI Engineer
CNAI
Seoul, Korea (Jul 2021 - May 2023)
ML Researcher
Seoul National University
Seoul, Korea (Mar 2017 - )
Undergraduate
Computer Science and Engineering
Publications
2025
Aligning Text to Image in Diffusion Models is Easier Than You Think
Jaa-Yeon Lee*, ByungHee Cha*, Jeongsol Kim*, Jong Chul Ye
Under review on Neurips 2025.
2024
CAS: A Probability-Based Approach for Universal Condition Alignment Score
Paper Project Page
Chunsan Hong*, ByungHee Cha*, Tae-Hyun Oh
Spotlight (Top 5%), ICLR, 2024.
ConfBALD: Deep Bayesian Active Learning against Confusing Classes
Chunsan Hong*, Dogyun Kim*, ByungHee Cha*, Bohyung Kim, Junsik Kim, Tae-Hyun Oh
Under review.
2023
Enhancing Classification Accuracy on Limited Data via Unconditional GAN
Paper
Chunsan Hong*, ByungHee Cha*, Bohyung Kim, Tae-Hyun Oh
ICCV workshop, 2023.