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Research Interests
Generative AI
We are developing state-of-the-art deep learning and machine learning methods to advance the fields of:
Deep generative models
Representation learning
Artificial general intelligence
Data-Centric AI
Data-Centric AI
We are interested in addressing the challenges of data in real-world scenarios, particularly in:
Domain adaptation (SFDA, TTA, ...)
Multimodal learning (VLM, VLA, MLLM, ...)
Biomedical data science
Projects
Scalable and Adaptive Multimodal AI for Artificial General Intelligence
(범용 인공지능을 위한 초확장·초적응 멀티모달 인공지능)
이화여자대학교 G-LAMP 사업단, Ministry of Education
2025.09 ~ 2030.08
Development of Generative AI to Overcome Data Limitations in Real-World Biomedical Foundation Models
(실세계 의생명 파운데이션 모델의 데이터 한계 문제 극복을 위한 생성형 인공지능 기반기술 개발)
우수신진연구, National Research Foundation (NRF) of Korea
2025.03 ~ 2030.02
TRUE-AI: Trustworthy and Resource-efficient Unified Evolving AI
(고신뢰 고효율 인공지능 교육연구단)
4단계 BK21 사업, Ministry of Education
2025.09 ~ 2027.08
scClovaX: Single-Cell AI using Foundational Model
(scClovaX: 파운데이션 모델을 이용한 단일세포 인공지능)
네이버 디지털 바이오 연구과제, Naver
2024.02 ~ 2027.01
Artificial Intelligence Graduate School Program (Ewha Womans University)
(인공지능융합혁신인재양성사업(이화여자대학교))
인공지능융합혁신인재양성사업, IITP
2025.03 ~ 2025.12
Healthcare Software Development and Platform Construction
(헬스케어 소프트웨어 개발 및 플랫폼 구축)
지자체-대학 협력기반 지역혁신사업 (RIS), Ministry of Education
2024.03 ~ 2024.12 (Completed)
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