We pursue research in machine learning and optimization. To this end,
we develop theories and algorithms using computational and
mathematical tools. Our ultimate goal is to provide robust and
provable solutions to challenging problems in artificial intelligence,
particularly those in large-scale settings. We are passionate about
translating our findings into practical applications that can benefit
society. For further details on our research directions and ongoing
projects, please refer to the
Research.
Recent News
Jul 2026
🏆 Kwanhee received the IITP President’s Award in the Graduate
Student Outstanding Research Achievement Competition at AI Connect Korea 2026.
Jul 2026
🏆 Our new paper on extreme LLM sparsity has received the Best Paper
Award at CKAIA 2026. The preprint will be available on arXiv soon.
Jul 2026
🏆 Sungbin has been selected for the 2026 PhD Excellence Scholarship
in Science and Engineering.
Jun 2026
🎓 Our paper on flatness-based out-of-distribution detection has been
accepted to ECCV 2026. In this work, we propose FOLD, a lightweight
detector that leverages feature-Hessian curvature and partial feature
normalization to improve ID-OOD separability.
May 2026
🏆 Our paper on uncertainty quantification in ICL, accepted to
ACL 2026, has been selected as an Oral presentation. In this work,
we introduce self-function vectors to directly decompose uncertainty,
and propose a novel framework to evaluate these disentangled sources.
If you want to see our earlier news, check out here!
Acknowledgements
Our research is generously supported by multiple organizations
including government agencies (NRF, IITP), industry (Google, Samsung,
Naver, Intel), and academic institutions (POSTECH, Yonsei).
