Seung Yul Lee — accepted AI-conference papers

2026

SpareTrain: Fault-Tolerant LLM Training via Low-Cost Dual Modular Redundancy

Rihae Park, Yeonjae Kim, Seung Yul Lee, Yeonhong Park, Jae W. Lee

ICLR 2026poster

Dual Modular Redundancy (DMR) is a highly effective mechanism for detecting silent data corruption (SDC)—a critical reliability concern in large language model (LLM) training—by executing each operation twice. However, its high computation overhead has prevented practical deployment at scale. In thi…

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2025

FlashTP: Fused, Sparsity-Aware Tensor Product for Machine Learning Interatomic Potentials

Seung Yul Lee, Hojoon Kim, Yutack Park, Dawoon Jeong, Seungwu Han, Yeonhong Park +1

ICML 2025spotlight

Machine Learning Interatomic Potentials (MLIPs) enable efficient molecular dynamics (MD) simulations with high accuracy. While equivariant MLIPs achieve state-of-the-art accuracy, they face significant computational bottlenecks centered around their Tensor-Product layer, which account for up to 75\%…

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