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
Aug 2026
🏆 Hyunji, Hyeondo, and Jinseok have been selected for the NRF Research Grant for Master’s and PhD Students.
Aug 2026
🏛 We are co-organizing the LeT Workshop on October 7~8 at POSTECH. The workshop aims to bring together researchers interested in machine learning theory.
Aug 2026
🎓 Our paper on extreme LLM sparsity has been accepted to EMNLP 2026. In this work, we show that carefully handling the elementary components of pruning can already substantially outperform existing pruning methods, even at extreme sparsity.
Aug 2026
🎓 Our paper on semiconductor anomaly detection has been accepted to BMVC 2026. In this work, we introduce a semiconductor defect dataset (OASIS) along with vision-guided instruction tuning method.
Jul 2026
🤝 Our lab has been selected for the 2026 NRF Next-Generation AI for Mathematics program.
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).
