Jaehyun Jang

Ph.D. Student @ KAIST AI Systems

prof_jaehyun.jpg

Department of AI Systems

KAIST

Daejeon, Republic of Korea 🇰🇷

Hello!

I am a Ph.D. student in the Department of AI Systems at the Korea Advanced Institute of Science and Technology (KAIST) and a member of the Artificial Intelligence & Machine Learning (U-AIM) Lab, advised by Professor Chang D. Yoo. Prior to this, I received my M.S. in Electrical Engineering from KAIST under the same advisor, and my B.S. in Smart Vehicle Engineering from Konkuk University.

My research focuses on enhancing the visual grounding and reasoning capabilities of Multimodal Large Language Models (MLLMs). Ultimately, I aim to advance Embodied and Agentic AI systems—spanning physical, interactive, and multi-agent AI—that bridge the gap between perception and action to operate reliably in complex environments.

news

News

Jun 2026 One paper accepted to ECCV 2026 (My first first-author paper).
Jun 2026 Successfully defended my master’s thesis and will begin my Ph.D. in Fall 2026.
May 2026 One paper accepted to ICML 2026 (Spotlight, top 2.2%).

Education

  • Korea Advanced Institute of Science and Technology (KAIST) , Daejeon, Republic of Korea
    Ph.D. Student in the Department of AI Systems
    Sep. 2026 - Present
    Advisor: Chang D. Yoo
  • Korea Advanced Institute of Science and Technology (KAIST) , Daejeon, Republic of Korea
    M.S. in Electrical Engineering
    Sep. 2024 - Aug. 2026
    Advisor: Chang D. Yoo
    GPA: 3.98/4.3
  • Konkuk University , Seoul, Republic of Korea
    B.S. in Smart Vehicle Engineering
    Mar. 2018 - Aug. 2024
    GPA: 4.38/4.5 (Summa Cum Laude)

Publications

★ denotes selected publications
[C]: Conference Paper [J]: Journal Paper [P]: Preprint

Projects

Honors and Awards

  • National Science & Technology Scholarships
    Korea Student Aid Foundation, Korea
    Mar 2023

Teaching Assistance

  • Introduction to Machine Learning 2025 Fall
  • AI for Robot Intelligence: Perception, Planning and Control 2026 Spring
  • Probability and Introductory Random Processes 2025 Spring