Prof.
Jaekoo Lee
이재구 교수
Machine Intelligence Laboratory
Education
학력
Ph.D. Seoul National University, Electrical and Computer Engineering ()
M.S. UC San Diego, Electrical and Computer Engineering ()
M.S. UC San Diego, Electrical and Computer Engineering ()
Career
경력
Present Associate Professor, Department of AI, Kookmin University
PI, AI Star Fellowship (Kookmin University)
Visiting Professor, UC Irvine, Computer Science
Data Scientist, SK Telecom AI Research Center
SW Research Engineer, LG Electronics R&D Campus
PI, AI Star Fellowship (Kookmin University)
Visiting Professor, UC Irvine, Computer Science
Data Scientist, SK Telecom AI Research Center
SW Research Engineer, LG Electronics R&D Campus
현재 국민대학교 인공지능학부 부교수
AI Star Fellowship PI (국민대학교)
UC Irvine 컴퓨터과학과 방문교수
SK텔레콤 AI Research Center 데이터 사이언티스트
LG전자 R&D Campus SW 연구원
AI Star Fellowship PI (국민대학교)
UC Irvine 컴퓨터과학과 방문교수
SK텔레콤 AI Research Center 데이터 사이언티스트
LG전자 R&D Campus SW 연구원
Research Overview 연구 개요
The Machine Intelligence Laboratory conducts research across three pillars: Intelligence Innovations (foundation models, generative AI), Applied Intelligence (robotics, autonomous vehicles, healthcare, security), and Systems for Intelligence (memory/storage systems, heterogeneous computing, edge AI). Recent publications include CVPR, WACV, IROS, AAMAS, and BMVC.
기계지능 연구실은 세 가지 축으로 연구를 수행합니다: Intelligence Innovations(파운데이션 모델, 생성형 AI), Applied Intelligence(로보틱스, 자율주행, 헬스케어, 보안), Systems for Intelligence(메모리/스토리지 시스템, 이기종 컴퓨팅, 엣지 AI). 최근 CVPR, WACV, IROS, AAMAS, BMVC 등 최상위 학회에 논문을 게재하고 있습니다.
Research Areas 연구 분야
- Foundation Models & Generative AI
- Computer Vision & 3D Understanding
- Robotics & Autonomous Driving
- Reinforcement Learning
- AI for Healthcare & Science
- 파운데이션 모델 및 생성형 AI
- 컴퓨터 비전 및 3D 이해
- 로보틱스 및 자율주행
- 강화학습
- 헬스케어 및 과학을 위한 AI
Major Achievements 주요 연구 성과
- FEAT: Fashion Editing and Try-On from Any Design (CVPR 2026)
- Training-Free Few-Shot Segmentation (WACV 2026)
- Text-Driven Semantic Variation for OOD Segmentation (IROS 2025)
- Leveraging Inductive Bias in ViT for Medical Image Diagnosis (BMVC 2024)
- Learning Temporal Cues for Multi-camera 3D Object Detection (ICRA 2024)
Recent Publications 주요 논문
- S. Kwon, G. Lee, D. Jung, and J. Lee, "FEAT: Fashion Editing and Try-On from Any Design," CVPR 2026
- E. Yoon, T. Park, and J. Lee, "Training-Free Few-Shot Segmentation via Vision-Language Guided Prompting," WACV 2026
- J. Eom, M. Kang, M. Yoon, N. Dutt, J. Kim, and J. Lee, "PRCSL: Privacy-preserving Continual Split Learning for Decentralized Medical Diagnosis," Machine Learning with Applications, 2026
- S. Song and J. Lee, "Leveraging Text-Driven Semantic Variation for Robust OOD Segmentation," IROS 2025
- S. Kim, J. Baek, J. Kim, and J. Lee, "GUIDE-CoT: Goal-driven and User-Informed Dynamic Estimation for Pedestrian Trajectory using Chain-of-Thought," AAMAS 2025
- J. Ha, E. Yoon, S. Kim, J. Kim, and J. Lee, "Leveraging Inductive Bias in ViT for Medical Image Diagnosis," BMVC 2024
- S. Moon, H. Park, J. Lee, and J. Kim, "Learning Temporal Cues by Predicting Objects Move for Multi-camera 3D Object Detection," ICRA 2024
- J. Lee and J. Lee, "Controllable 3D Object Generation with Single Image Prompt," ICPR 2024