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.
May 2026
🏆 Our paper on uncertainty quantification in ICL, accepted to
ACL 2026, has been selected as an Oral presentation.
May 2026
🎓 Our paper on asynchronous pipeline parallelism has been
accepted to ICML 2026. In this work, we propose a basis rotation
approach to effectively address the associated gradient staleness
problem.
Apr 2026
🎓 Our paper on uncertainty quantification in ICL has been
accepted to ACL 2026. In this work, we introduce self-function
vectors to directly decompose uncertainty, and propose a novel
framework to evaluate these disentangled sources.
Apr 2026
🤝 Our lab has been selected for the IITP Efficient AI National
R&D Program (2026–2029), a national research program with
KRW 7.5B in total funding, together with leading partners
including SqueezeBits.
Feb 2026
🤝 Our lab has been selected for the "Early-Career Researcher
Infrastructure Support Program" (NRF) — one of the most
competitive grants in Korea. With 500M KRW in funding, we
will build a high-performance compute infrastructure to power our
research on extreme compression of hyperscale AI foundation
models.
Our research is generously supported by multiple organizations
including government agencies (NRF, IITP), industry (Google, Samsung,
Naver, Intel), and academic institutions (POSTECH, Yonsei).
