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
- Feb 2026 Our lab has been selected for the Young Investigator Infrastructure Support Program funded by National Research Foundation of Korea.
- Feb 2026 📃 Our new paper is now available on arXiv! In this work, We introduce SeedFlood, a decentralized LLM training framework that enables model-size–independent communication cost and perfect consensus.
- Feb 2026 📃 Our new paper is now available on arXiv! In this work, we propose a basis rotation approach to effectively address the gradient staleness problem in asynchronous pipeline parallelism.
- Feb 2026 ✈️ Kwanhee started as a visiting student researcher in the Dan Alistarh Group.
- Jan 2026 🎓 Our paper on extreme LLM sparsity (ELSA) has been accepted to ICLR 2026.
- Nov 2025 🏆 Dongyeop recived the Qualcomm Innovation Fellowship Korea 2025.
Acknowledgements
Our research is generously supported by multiple organizations including government agencies (NRF, IITP), industry (Google, Samsung, Naver, Intel), and academic institutions (POSTECH, Yonsei).