Heading to ICML 2026
I will be attending the International Conference on Machine Learning this July.
M.S. Student at KAIST School of Computing
I benchmark AI models, study their failure modes, and explore mechanistic interpretability to help make AI systems more transparent and reliable.
About me
Hello! I am a first-year master's student at KAIST's School of Computing, advised by Professor Alice Oh in the U&I Lab.
My research focuses on benchmarking AI models and measuring their robustness to identify failure modes, with the broader goal of making AI systems more transparent and reliable. More recently, I have become interested in mechanistic interpretability as a way to look beyond model outputs and better understand the internal representations and computations that drive model behavior.
I am always open to new collaborations. Please feel free to reach out at [email protected].
What Iām up to
I will be attending the International Conference on Machine Learning this July.
Our multicultural, multimodal, and multilingual benchmark for AI risk and reliability is now available on arXiv.
World in a Frame explores culture mixing as a new challenge for vision-language models.
When Tom Eats Kimchi received the Outstanding Paper award at C3NLP 2025.