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
Oct 2026
🧑💻 Minkyoung has started a research internship at Nota AI.
Oct 2026
🎓 Our paper on hardware-native sparse-quantization for trillion-scale MoEs is out on arXiv. In this work, we introduce MoESQ, which jointly learns semi-structured sparsity and low-precision quantization for MoE experts and executes them on Sparse Tensor Cores with a custom grouped sparse GEMM kernel. The code and models are publicly available.
Sep 2026
🎓 Our paper on asynchronous pipeline parallelism has been accepted to NeurIPS 2026. In this work, we introduce FRESCO, a new principle for consistency control in asynchronous pipelines, which consistently outperforms standard baselines while being extremely efficient and theoretically sound.
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.
If you want to see our earlier news, check out here!