NAACL 2025: 'Evaluating Multimodal Generative AI with Korean Educational Standards' – introduced KoNET, a Korean multimodal benchmark based on standardized tests
ICLR 2025: 'How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?' – proposed a weight merging method to preserve safety and capability
EMNLP 2024: 'On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding' – developed a more efficient VLM
ACL 2024 Findings: 'Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation' – created an open-source VLM for fine-grained evaluation
EMNLP 2023: 'Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models' – introduced the Cream model for document understanding
Research Experience
Applied Research Scientist and Tech Lead at NAVER Cloud, managing multiple R&D projects
Led open-source projects including Donut, Webvicob, and Cream
Developed LLM-based multimodal products such as HyperCLOVA X Vision
Conducting Ph.D. research at KAIST AI on multimodal machine learning
Part-time lecturer at the University of Seoul teaching AI engineering
Background
Currently serving as an Applied Research Scientist and Technical Leader (Tech Lead) at NAVER Cloud
Research interests broadly span Multimodal Machine Learning (Vision, Language, and Audio)
Conducts research and software engineering for NAVER's LLM-based multimodal solutions and products (e.g., HyperCLOVA X Vision)
Pursuing a Ph.D. in Artificial Intelligence at KAIST AI under the supervision of Prof. Minjoon Seo
Serves as a part-time lecturer at the University of Seoul, teaching 'AI Engineering in Production'
Focuses on building robust and generalizable machine learning systems for real-world applications