Jaewoo Lee | KAIST

About Me

Thanks for visiting my website!😄
I received a M.S. degree in Artificial Intelligence at KAIST, MLAI Lab (Advisor: Prof.Sung Ju Hwang) in Aug 2024, and a B.S. degree in Electrical Engineering at KAIST in Feb 2023.
I’m currently working as an AI researcher at DeepAuto.

Research Interests

My goal is to make AI more accessible in everyday life, with research focused on

Multimodal (Vision-Language, Audio-Video, Audio-Image): My goal is to develop multimodal models that enhance human-machine interaction and increase AI accessibility in everyday life.
Efficiency (Data pruning, Model compression, Token Selection): To further enhance the AI accessibility, I aim to minimize AI training & inference costs by developing algorithms that identify data or model structure redundancies.
Continual Learning: I am also interested in developing algorithms that keep models up-to-date by effectively integrating emerging knowledge.
Curriculum Learning: I aim to discover the structure of skills in Large Vision-Language Models to optimize data order in instruction tuning, enhancing training efficiency (Empirical Study).

📝 Publications

(* denotes the equal contribution and ^ denotes the equal advising)

Preprint

Unified Multi-Modal Interleaved Document Representation for Information Retrieval

Jaewoo Lee*, Joonho Ko*, Jinheon Baek*, Soyeong Jeong, Sung Ju Hwang

Preprint.

PDF

EMNLP

Concept-skill Transferability-based Data Selection for Large Vision-Language Models

Jaewoo Lee, Boyang Li, Sung Ju Hwang

Conference on Empirical Methods in Natural Language Processing, Main conference Long paper (EMNLP), 2024.

PDF Code

ICML

STELLA: Continual Audio-Video Pre-training with Spatio-Temporal Localized Alignment

Jaewoo Lee*, Jaehong Yoon*, Wongjae Kim, Yunji Kim, Sung Ju Hwang

International Conference on Machine Learning (ICML), 2024.

PDF Code Project Page

ICSV

Sound-based drone fault classification using multitask learning

Wonjun Yi, Jung-Woo Choi, Jaewoo Lee

International Congress on Sound and Vibration (ICSV), 2023.

PDF Dataset

📖 Educations

2023.03 - 2024.08, M.S. in Artificial Intelligence. KAIST.
- Thesis: Efficient Training Techniques for Multimodal Learning (PDF)
- A half-year early graduation.
2020.03 - 2023.02, B.S. in Electrical Engineering. KAIST.
- Summa Cum Laude
- One-year early graduation.

💻 Research Experiences

2023.08 - current, AI Researcher, DeepAuto, South Korea.
2023.03 - 2024.08, Master degree Student, MLAI Lab, KAIST, South Korea.
2022.07 - 2023.02, Research Intern, MLAI Lab, KAIST, South Korea.
2021.09 - 2022.06, Research Intern, Smart Sound Systems Lab, KAIST, South Korea.
2021.06 - 2021.08, Research Intern, Urban Robotics Lab, KAIST, South Korea.

🎖 Honors and Awards

KAIST Summa Cum Laude Award, Feb. 2023
National Scholarship for Science & Engineering, Sep. 2022 - Feb. 2023
Encouragement Award for the Undergraduate Research Program, Aug. 2022
KAIST Dean’s List (College of Engineering), Aug. 2022
KAIST Dean’s List (School of Freshman), Aug. 2020