DAIS Research Group

The DAIS Research Group is an AI and data science research group within the Energy AI & Computational Science Laboratory at the Korea Institute of Energy Research (KIER). We use complex, real-world data from energy and scientific research as both a starting point and a testbed for studying generalizable AI methodologies, including machine learning, scientific document understanding, knowledge-based reasoning, and AI agents. Rather than building one-off solutions for individual projects, we develop models, evaluation methods, and data-processing pipelines that can transfer across scientific and industrial domains, connecting methodological advances with academic contributions and practical research tools.

Data โ†’ Analysis โ†’ Intelligence โ†’ System

  • D โ€” Data. Collecting and organizing the diverse research data that arises in the field โ€” unstructured documents (PDFs, reports, papers), experimental and process measurements, and more.
  • A โ€” Analysis. Structure analysis and information extraction from documents; preprocessing of experimental and process data into shapes downstream models can use.
  • I โ€” Intelligence. Classification, summarization, search, and recommendation over large document and data collections; prediction and fitting models for experimental and process data; robust learning methods combined with domain knowledge.
  • S โ€” System. Turning models and analyses into things other people can actually use; automating repetitive work and keeping analysis code reproducible.

We collaborate with other groups within KIER and with external partners, organizing our work as reusable code and workflows rather than one-off solutions tied to a single project.

Recent Award
Hyoeun Choi and Woonghee Lee at the ASK 2026 award ceremony
After the ASK 2026 award ceremony

๐Ÿ† Bronze Award

Undergraduate / High-School Paper Competition
ASK 2026 (Annual Symposium of the Korea Information Processing Society)
May 21, 2026, Gangneung, South Korea

Awardee: Hyoeun Choi (Chungnam National University, research intern)

Paper: Comparing SHAP and CRAFT Across Architectures for PEMFC SEM Images

View paper โ†’

Publications
Domestic Conference Papers
(2026). Comparing SHAP and CRAFT Across Architectures for PEMFC SEM Images. ASK 2026 ยท ๐Ÿ† Bronze Award (Undergraduate Track).
Software Copyrights
Media & Outreach
Members

Principal Investigator

Woonghee Lee

Woonghee Lee, Ph.D.

Senior Researcher

Energy AI & Computational Science Lab., KIER

Email ยท CV โ†—

Undergraduate Interns

Yunyeong Ju

Yunyeong Ju

Research Intern (2026-07 โ€“ 2026-12)

Korea University of Technology and Education (KOREATECH)
Computer Vision

GitHub โ†—

Dongwan Yoo

Dongwan Yoo

Research Intern (2026-07 โ€“ 2026-12)

Chungnam National University
LLM Architecture ยท Machine Unlearning

GitHub โ†—

Alumni

Hyoeun Choi

Hyoeun Choi

Research Intern (2026-03 โ€“ 2026-06)

Chungnam National University
Computer vision ยท XAI

Bronze Award, ASK 2026 Undergraduate / High-School Paper Competition

GitHub โ†—

Gallery

KDD 2026 โ€” Jeju Island ยท August 2026

Woonghee Lee, Dongwan Yoo, and Yoonyoung Joo at KDD 2026
From left: Woonghee Lee, Dongwan Yoo, and Yoonyoung Joo

View the full gallery โ†’

Contact

152 Gajeong-ro, Yuseong-gu, Daejeon 34129, South Korea

Send email โ†— View meeting availability โ†—

For meetings or collaboration discussions, please contact us by email.