PeopleofSuperintelligence

Researcher

Jongseok Lee

Also known as Jong-Seok Lee, 이종석

Jongseok Lee is an associate professor of industrial and systems engineering at KAIST who leads the AIME Laboratory and develops machine-learning methods for imbalanced data, monotonic neural networks and physics-informed neural networks. After earning degrees from Inha University in 2003 and POSTECH in 2005 and working as a POSTECH researcher until 2006, he completed an industrial engineering PhD at Iowa State University in 2009 and developed statistical software at SAS Institute until 2012. From 2012 to 2024, he taught in the Department of Systems Management Engineering at Sungkyunkwan University, where he published the AUC4.5 decision tree algorithm in 2019 and coauthored Scalable Monotonic Neural Networks with Hyunho Kim at ICLR 2024. Since September 2024, he serves on the KAIST faculty, coauthoring the Subject-Adaptive Sparse Linear framework in October 2025, the HARMONIC training method at ICLR in April 2026, and noise-aware AdaBoost in Pattern Recognition in September 2026.

Now at
KAIST
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Timeline

  1. March 1996–February 2005

    Degrees from Inha and POSTECH

    Jongseok Lee studies industrial engineering at Inha University in Incheon from March 1996 to February 2003 and earns a Bachelor of Science. A Master of Science in information technology at the Pohang University of Science and Technology (POSTECH) in Pohang follows between March 2003 and February 2005.

  2. March 2005–June 2006

    Researcher at POSTECH

    After completing his master's degree, he works as a researcher at the Pohang University of Science and Technology in South Korea from March 2005 to June 2006.

  3. August 2006–August 2009

    PhD at Iowa State University

    At Iowa State University in Ames, Iowa, he serves as a teaching and research assistant from August 2006 to August 2009 and receives a PhD in industrial engineering. During his doctoral studies, he works as a graduate intern in JMP at SAS Institute in the United States from May 2008 to August 2008.

  4. May 2009–January 2012

    Software engineer at SAS Institute

    He joins SAS Institute in the United States as an analytics software engineer from May 2009 to January 2012. Working in the Research and Development Group, he focuses on statistical software development.

  5. March 2012–August 2024

    Professor at Sungkyunkwan University

    Returning to South Korea, Lee joins the Department of Systems Management Engineering at Sungkyunkwan University as an assistant professor from March 2012 to February 2018. He serves as an associate professor in the department from March 2018 to August 2024.

  6. 2019

    The AUC4.5 decision tree algorithm

    In 2019, IEEE Access publishes his paper "AUC4.5: AUC-based C4.5 decision tree algorithm for imbalanced data classification." The algorithm modifies Quinlan's C4.5 decision tree method for imbalanced data classification.

  7. 2024

    Scalable monotonic neural networks

    At ICLR 2024, his poster "Scalable Monotonic Neural Networks" with Hyunho Kim introduces a partially connected network architecture. Its scalable monotonic hidden layer combines an exponentiated unit, a ReLU unit and a confluence unit, and the network trains with ordinary backpropagation.

  8. Since September 2024

    Associate professor at KAIST

    Since September 2024, Lee is an associate professor at the Korea Advanced Institute of Science and Technology (KAIST) in the Department of Industrial and Systems Engineering and the Graduate School of Data Science, where he leads the AIME Laboratory. In October 2025, he coauthors the Subject-Adaptive Sparse Linear (SASL) framework, which integrates ordinary least squares regression with subject-specific interactions for personalized health prediction. His 2026 work includes an April 2026 ICLR poster with Dohyun Bu and Yujung Byun that proposes the HARMONIC training procedure for physics-informed neural networks with nonconvex convergence guarantees, and a September 2026 Pattern Recognition paper with B. Ku on noise-aware weight updating in AdaBoost.

Career

  1. Since 2024KAISTAssociate Professor
  2. 2012–2024Sungkyunkwan UniversityAssistant Professor, Associate Professor
  3. 2009–2012SAS InstituteAnalytics software engineer

Education

  1. 2009Iowa State UniversityPhD in industrial engineering
  2. 2005Pohang University of Science and TechnologyMaster's degree in information technology
  3. 2003Inha University

From the biography.

  1. The United States employs an estimated 48% of people shaping AIIncludes KAIST. We estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  2. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes KAIST. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  3. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes KAIST. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  4. 44% of AI career moves go from one university to anotherIncludes KAIST. We estimate how 26,439 AI career moves link universities, companies, labs, nonprofits and government research centers, with decade counts, net flows and the top paths.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Jongseok Lee yet. Send us a free-licensed photo.

  1. Professor | AIME Lab. (Jong-Seok Lee, KAIST) aime.kaist.ac.kr. Checked 22 October 2026.
  2. [ISE Colloquium] Monotonicity as an Inductive Bias for Neural Network Training / Prof. JongSeok Lee / KAIST | KAIST Graduate School of Data Science gsds.kaist.ac.kr. Checked 22 October 2026.
  3. Jong-Seok Lee - Google Scholar scholar.google.com. Checked 22 October 2026.
  4. AUC4.5: AUC-Based C4.5 Decision Tree Algorithm for Imbalanced Data Classification pure.kaist.ac.kr. Checked 22 October 2026.
  5. ICLR Poster Scalable Monotonic Neural Networks iclr.cc. Checked 22 October 2026.
  6. Subject-Adaptive Sparse Linear Models for Interpretable Personalized Health Prediction from Multimodal Lifelog Data arxiv.org. Checked 22 October 2026.
  7. ICLR Poster Harmonized Cone for Feasible and Non-conflict Directions in Training Physics-Informed Neural Networks iclr.cc. Checked 22 October 2026.
  8. Jong-Seok Lee - Korea Advanced Institute of Science and Technology pure.kaist.ac.kr. Checked 22 October 2026.