johnjaejunlee95's Easy AI Blog

Hello! I am @johnjaejunlee95, a 5th-year M.S./Ph.D. integrated student at the Graduate School of Artificial Intelligence, UNIST.

My research stems from a fundamental question: “How do AI models learn and generalize?”

  • Past: Meta-Learning, Generalization
  • Current: Multimodal Learning (Theoretical Approach & Applications e.g., VLA), Flatness (Loss Landscape Perspective), AI4Science (Bio)

While my earlier research focused on Meta-Learning, I am currently conducting research on Multimodal Learning. I focus on analyzing Multimodal Learning through a theoretical lens, specifically connecting it to the geometry of the Loss Landscape (Flatness). Recently, I have also developed an interest in Bioinformatics (e.g., EEG) and have begun exploring the field of AI4Science.

My goal extends beyond merely building high-performance models; I aim to become a researcher who “deeply understands AI based on solid theoretical and mathematical grounds and explains it clearly to the audience.” I aspire to uncover the operating principles of models both intuitively and theoretically, and to act as a bridge, conveying complex engineering concepts to the public in an intuitive and approachable way.

This blog will primarily feature posts on:

  • Deep Learning: Concepts, Algorithms & Intuition
  • Essential Mathematics for AI
  • Paper Reviews
🧾 My CV (click to expand)

🎓 Education


  • Ulsan National Institute of Science and Technology (UNIST)
    M.S.-Ph.D. Combined Candidate — Graduate School of Artificial Intelligence (AIGS)
    Mar. 2022 - Present · GPA: 3.97 / 4.30
  • Dankook University
    B.S. — Department of Electronics and Electrical Engineering (EE)
    Mar. 2015 - Feb. 2022 · GPA: 3.93 / 4.50

🧪 Experience


  • Machine Intelligence and Information Theory Lab (MIIT)
    M.S.-Ph.D. Combined Research Student @ Ulsan National Institute of Science and Technology (UNIST)
    Mar. 2022 - Present
    • Research in meta-learning, multimodal learning, and robust VLA.
  • Design Technology Laboratory (DTL)
    B.S. Internship @ Yonsei University
    Feb. 2021 - Aug. 2021
    • Worked on practical ML/mobile implementation projects.
  • SoC and Embedded System (SES)
    B.S. Internship @ Dankook University
    Jul. 2020 - Feb. 2021
    • Worked on embedded/SoC-related research and development tasks.

📝 Publications


  • Understanding Multimodal Learning: A Loss Landscape Smoothness Perspective
    Jae-Jun Lee, Sung Whan Yoon
    Forty-third International Conference on Machine Learning (ICML), (2026)
  • Can One Modality Model Synergize Training of Other Modality Models?
    Jae-Jun Lee, Sung Whan Yoon
    The Thirteenth International Conference on Learning Representations (ICLR), Singapore (2025)
  • XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage
    Jae-Jun Lee, Sung Whan Yoon
    International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain (2024)

🧾 Patents


  • Single modality model-based multimodal learning device and method thereof
    Sung Whan Yoon, Jae-Jun Lee
    KR 10-2025-0066240 · Publication: May 21, 2025
  • Model-agnostic meta learning (MAML)-based domain adaptation algorithm to improve the effectiveness of developing biomedical artificial intelligence (AI) systems
    Han-Jeong Hwang, Sung Whan Yoon, Ga-Young Choi, Jae-Jun Lee
    KR 10-2024-0176258 · Publication: Dec. 02, 2024

🧩 Projects


AI-based Cone Beam CT Image Enhancement (Mar. 2026 - Dec. 2026)
Sponsor: Dongnam Institute of Radiological & Medical Sciences
Role: Core member (MIIT, UNIST)
  • Development of generative model-based image enhancement for Cone Beam CT (CBCT) images.
  • Fully contributing as a core member (MIIT, UNIST).
Virtual Tactile Signal Generation Based on Multimodal Vision-Tactile AGI (Sept. 2025 - Dec. 2025)
Sponsor: IITP
Role: Core member (MIIT, UNIST)
Partners:
- Research: Korea University Sejong Campus, UNIST, Technische Universitat Berlin
- Industry: Immertix, PSDL, SURROMIND, Sotong Five, Nongshim Data System (NDS)
  • Development of 3D reconstruction containing tactile information of each segmented object.
  • Fully contributed as a core member (MIIT, UNIST).
RL Solution for High-Efficiency and Reliable Combustion System (Mar. 2022 - Aug. 2023)
Sponsor: Samyang Corp. & UNIST
Role: Core member (MIIT, UNIST)
  • Designed a smart control system for combustion processes using deep reinforcement learning to improve efficiency and reliability.
  • Fully contributed as a core member (MIIT, UNIST).
Heterogeneous Home Appliance Data Analysis Framework (Jul. 2022 - Jan. 2023)
Sponsor: LG H&A
Role: Core member (MIIT, UNIST)
  • Designed a framework to identify causal relationships among heterogeneous smart home appliances using CCM and Meta-Learning under limited data.
  • Fully contributed as a core member (MIIT, UNIST).
Identification Data Processing Technology for On-Site Police Use (Apr. 2021 - Aug. 2021)
Sponsor: National Research Foundation of Korea (NRF)
Role: Project member (DTL, Yonsei University)
  • Developed a mobile app for facial verification for on-site use cases including missing-child identification.
  • Built with Android Studio and Java.

🧑‍🏫 Teaching & Service


📚 TA

  • Non-Parametric Bayesian Course
    Teaching Assistant · Graduate School of Artificial Intelligence, UNIST
    Sep. 2023 - Dec. 2023
  • Meta & Multi-task Learning Course
    Teaching Assistant · Graduate School of Artificial Intelligence, UNIST
    Sep. 2023 - Dec. 2023

🏫 Special Program

  • 2023 ROK Navy AI Specialist Program
    Coding sessions on fundamentals of CNN & RNN · Republic of Korea Naval Academy
    Aug. 2023 - Aug. 2023
  • LG Electronics DX Intensive
    Coding sessions on the Principles of Deep Learning · UNIST
    Summer 2024 - Summer 2024
  • LG Electronics DX Intensive
    Coding sessions on the Principles of Deep Learning · UNIST
    Summer 2022 - Summer 2022

🌱 AI Novatus Academia

  • AI Novatus Academia
    Coding sessions on the Basics of AI for Industrial Engineers · Changwon & Ulsan
    Spring 2024 - Spring 2024
  • AI Novatus Academia
    Coding sessions on the Basics of AI for Industrial Engineers · Changwon & Ulsan
    Spring 2023 - Spring 2023
  • AI Novatus Academia
    Coding sessions on the Basics of AI for Industrial Engineers · Changwon & Ulsan
    Spring 2022 - Spring 2022

🛠️ Skills


Programming Languages
Python · MATLAB (basic understanding) · C/C++ (basic understanding)
Tools & Platforms
Linux · Bash · Git · Conda
Frameworks & Libraries
Pytorch · JAX (basic understanding) · Scikit-Learn · Pandas · Huggingface
Mathematical Backgrounds
Linear Algebra · Probability & Statistics · Information Theory (basic understanding) · Estimation Theory (basic understanding) · Learning Theory (basic understanding)
Writing Tools
Latex · Markdown · Notion
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