Tae Jun Ham — accepted AI-conference papers

2025

Reliable and Cost-Effective Exploratory Data Analysis via Graph-Guided RAG

Mossad Helali, Yutai Luo, Tae Jun Ham, Jim Plotts, Ashwin Chaugule, Jichuan Chang +2

EMNLP 2025

Automating Exploratory Data Analysis (EDA) is critical for accelerating the workflow of data scientists. While Large Language Models (LLMs) offer a promising solution, current LLM-only approaches often exhibit limited accuracy and code reliability on less-studied or private datasets. Moreover, their

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2022

"L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training"

Jonghyun Bae, Woohyeon Baek, Tae Jun Ham, Jae W. Lee

ECCV 2022poster

"The training process of deep neural networks (DNNs) is usually pipelined with stages for data preparation on CPUs followed by gradient computation on accelerators like GPUs. In an ideal pipeline, the end-to-end training throughput is eventually limited by the throughput of the accelerator, not by t…

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