Kwanhee Lee

bassist_kwan.JPG

Hi, I’m a second-year Master’s student at Graduate School of Artificial Intelligence, POSTECH, where I’m fortunate to be advised by Prof. Namhoon Lee. Previously, I was an Academic Visitor in DASLab at ISTA, hosted by Prof. Dan Alistarh.

My goal is to make foundation models more efficient through the lens of optimization. Broadly, my research interests lie in 1. Optimization-based model compression (pruning, quantization, distillation), 2. Systematic optimization for efficient model inference (writing CUDA kernels, profiling/optimizing inference systems–vLLM), and 3. Optimization for deep learning (Muon/shampoo/soap and it’s friends!).

For more details, please take a look at my CV

news

May 23, 2026 We released Kimi-K2.5-P48-NVFP4-W4A4-Preview — Kimi-K2.5 with paired 4:8 sparsity and NVFP4 W4A4 quantization, targeting Blackwell sparse tensor cores. More coming soon!
Apr 19, 2026 Will be serving as a reviewer for NeurIPS 2026 and attending ICLR 2026 in Rio de Janeiro, 🇧🇷. See you in Rio!
Jan 26, 2026 Our paper on extreme LLM sparsity has been accepted to ICLR 2026!
Jan 05, 2026 Our paper SAFE won the Best Paper Award at POSTECH-GSAI BK21 2025!
Nov 17, 2025 Our new paper on extreme LLM sparsity won Best Paper Award at JKAIA 2025!

selected publications

  1. unseen_frontier.png
    The Unseen Frontier: Pushing the Limits of LLM Sparsity with Surrogate-Free ADMM
    Kwanhee Lee, Hyeondo Jang, Dongyeop Lee, and 2 more authors
    In ICLR, 2026
    JKAIA 2025 Best Paper Award
  2. safe.png
    SAFE: Finding Sparse and Flat Minima to Improve Pruning
    Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and 1 more author
    In ICML, 2025
    Spotlight(top 2.6%) poster | 2025 POSTECH-GSAI BK21 Best Paper Award
  3. dynamic.jpg
    Dynamic Network Compression via Probabilistic Channel Pruning
    Kwanhee Lee, and Hyang-Won Lee
    Neural Networks, 2026