I am an applied AI researcher who connects research outcomes to working systems. My work spans conversational AI, healthcare AI, and MLOps-oriented delivery from problem framing to implementation and operation.
I am interested in applied AI problems grounded in real-world data, and in connecting them to models, evaluation, and deliverables that collaborators can actually use. Recently, I have been working across conversational AI, medical AI, and ML systems with a focus on improving reproducibility and practical delivery.
This section highlights only the strongest projects so the problem, role, and outcome can be understood quickly.
Graduate Researcher
Nursing surveillance required diagnosis-related classification, but key clinical signals were fragmented across heterogeneous EMR sources.
Builder
Developers working alone often need UI, performance, or code quality feedback, but they rarely have an easy way to gather role-specific input at the right time.
Intelligent Data Analytics Lab., Gachon University
Led graduate research on EMR-based nursing surveillance decision support and diagnostic classification.
National Research Foundation of Korea (NRF)
Contributed to an NRF-funded clinical AI project centered on nursing surveillance decision support using EMR data.
Institute of Information & Communications Technology Planning & Evaluation (IITP)
Implemented evaluation-related code in a human-centered multimodal AI project.
The ability to refine real-world data for reliable modeling through analysis and preprocessing
The ability to turn domain-specific NLP into concrete tasks through experimentation and implementation
The ability to build repeatable workflows and scale them through deployment and automation
An operational perspective for tracing model behavior to improve reproducibility and maintenance
A concise overview of AI research achievements, MLOps-related technical stacks, and key project contributions.
Open CV/ResumeA case-study-based portfolio documenting each project's problem definition, architecture design, and process of overcoming technical limitations in detail.
Open PortfolioMathematics (submitted)
This study proposes a hierarchical clinical decision support framework that estimates diagnostic context via partial-label automated ICD coding and reinjects it into irregular ICU time-series forecasting through context-adaptive gating for mechanical ventilation transition prediction.
Journal of The Korea Society of Computer and Information
This study proposes an automatic ICD coding model for nursing surveillance of abdominal surgery patients by integrating EMR-based test data, patient information, and nursing notes.