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Youngdo Lee

M.S. Candidate

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lyd0531@kaist.ac.kr


I am a M.S. student at Korea Advanced Institute of Science and Technology (KAIST), advised by Prof. Jaegul Choo. Prior to this, I received my B.S. degree in Computer Science and Mathematics. My ultimate research goal is to contribute to the development of Artificial General Intelligence (AGI) capable of learning from minimal data and solving complex, real-world tasks that remain challenging for current AI models. Specifically, my recent research interests focus on the following areas:

  • Scalable & Sample-Efficient Deep Reinforcement Learning: Designing efficient RL architectures that can scale with larger networks and increased computational resources.

I am also interested in developing humanoid systems that can function naturally in human environments, assisting with everyday activities and enabling rich human–robot interaction. To address the scarcity of real-world physical data, I aim to advance data-efficient and generalizable learning approaches, building on my experience in large-scale simulation, synthetic experience generation, and RL.



Publications

    • Selected
    • All
  1. ICML
    Hyperspherical Normalization for Scalable Deep Reinforcement Learning
    Hojoon Lee$^\dagger$, Youngdo Lee$^\dagger$, Takuma Seno, Donghu Kim, Peter Stone, Jaegul Choo
    ($\dagger:$ Equal contribution)
    ICML 2025 Spotlight Presentation (313/12107 = 2.6%)
    PDF Code Project Page BibTex

  2. Review
    Unleashing the Architectural Potential of RL in Visual Continuous Control
    Donghu Kim$^\dagger$, Youngdo Lee$^\dagger$, Hojoon Lee, Johan Obando-Ceron, Byungkun Lee, Aaron Courville, Pablo Samuel Castro, Jaegul Choo, Clare Lyle
    ($\dagger:$ Equal contribution)
    Under Review
    PDF Code Project Page BibTex

  3. ICML
    Hyperspherical Normalization for Scalable Deep Reinforcement Learning
    Hojoon Lee$^\dagger$, Youngdo Lee$^\dagger$, Takuma Seno, Donghu Kim, Peter Stone, Jaegul Choo
    ($\dagger:$ Equal contribution)
    ICML 2025 Spotlight Presentation (313/12107 = 2.6%)
    PDF Code Project Page BibTex

  4. Review
    Extracting Any Concepts from an Image for Controllable Text-to-Image Generation
    Seunghwan Choi, Jooyeol Yun, Youngdo Lee, Jaegul Choo
    Under Review
    PDF

  5. KCC & ICMLW
    Abstraction and Reasoning Challenge with Decision Transformer
    Jaehyun Park, Jaegyun Im, Youngdo Lee, Donghyeon Shin, Sejin Kim, Sundong Kim
    Korea Computer Congress (KCC), 2023.
    PDF ICMLW version


Projects

    • Selected
    • All
  1. Real-World Humanoid Locomotion on Uneven Terrain with Sim-to-Real RL
    Developing resilient humanoid locomotion capable of handling irregular and unpredictable terrain using large-scale sim-to-real reinforcement learning.
    KRAFTON AI, Physical AI Team

  2. Real-World Humanoid Locomotion on Uneven Terrain with Sim-to-Real RL
    Developing resilient humanoid locomotion capable of handling irregular and unpredictable terrain using large-scale sim-to-real reinforcement learning.
    KRAFTON AI, Physical AI Team


Work Experience

  1. KRAFTON AI
    [Jun. 2025 - Present]
    Physical AI Team, Research Intern
    Mentor: Kangwook Lee
    TBD

  2. IBS
    [Dec. 2022 - Jun. 2023]
    Data Science Group, Research Intern
    Mentor: Meeyoung Cha and Sundong Kim
    Participated in Abstraction and Reasoning project
    Summary

  3. NCSOFT
    [Jul. 2022 - Aug. 2022]
    Speech AI Lab, Music AI Team, Research Intern
    Mentor: Gyeonghoon Lee
    Developed baseline project for deep learning-based melody generation


Education

  • Korea Advanced Institute of Science and Technology (KAIST)
    [Aug. 2024 - Present]
    M.S. Student in Artificial Intelligence
    • GPA: 4.24 / 4.30

  • Korea Advanced Institute of Science and Technology (KAIST)
    [Mar. 2020 - Aug. 2024]
    B.S. degrees in Computer Science and Mathematics
    • GPA: 4.03 / 4.30 (Summa Cum Laude)



Awards & Honors

  • KAIST Dean's List Award
    [Fall 2022, Spring 2023]
    Awarded to the top 3% of academic performance in KAIST
  • KAIST Presidential Fellowship
    A program that supports a small number of top students to grow as future global leaders in science and technology



Teaching

  • Teaching Assistant
    • CS101: Introduction to Programming, Spring 2024