Sungbin Shin

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Hi, I am a Ph.D student in computer science and engineering at POSTECH, advised by Prof. Namhoon Lee.

I am broadly interested in large-scale optimization for foundation models, with a focus on distributed and collaborative learning. I explore the algorithmic foundations that enable efficient, scalable training of billion-scale models across diverse participants, developing principled methods that push the boundaries of scalable machine learning.

Contact me via sungbin.shin@postech.ac.kr if you have any questions.

news

Jul 2026 I will be presenting our paper on basis rotation for asynchronous pipeline parallelism at ICML 2026 in Seoul 🇰🇷, including at the ICML Workshop: Protocol Learning organized by Pluralis Research. Come find me if you’re around, or feel free to reach out!
May 2026 Honored to be recognized as a Gold Reviewer (Top 25%) for ICML2026! 🥇
May 2026 Our paper on asynchronous pipeline parallelism has been accepted at ICML 2026! See you in Seoul 🇰🇷!
Feb 2026 Check out our new paper on asynchronous pipeline parallelism.
Nov 2025 I began serving as a reviewer for TMLR.

selected publications

2026

  1. Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation
    Hyunji Jung*, Sungbin Shin*, Namhoon Lee
    ICML, 2026

2025

  1. Critical Influence of Overparameterization on Sharpness-aware Minimization
    Sungbin Shin*, Dongyeop Lee*, Maksym Andriushchenko, Namhoon Lee
    UAI, 2025; ICML Workshop on High-dimensional Learning Dynamics, 2023
    Best paper award at JKAIA 2023

2024

  1. Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization
    Sungbin Shin, Wonpyo Park, Jaeho Lee, Namhoon Lee
    EMNLP, 2024

2023

  1. A Closer Look at the Intervention Procedure of Concept Bottleneck Models
    Sungbin Shin, Yohan Jo, Sungsoo Ahn, Namhoon Lee
    ICML, 2023; NeurIPS Workshop on TSRML, 2022