Need role fit and official record first?
Experience, publications, selected work
A portfolio of implemented work across data structure, AI development workflows, and NLP/LLM evaluation.
I organize structured, text, image, and graph data into model-usable forms, then connect experimentation, evaluation, deployment, and review decisions into implemented work.
Different reviewers need different first evidence, so the site routes by review task.
Experience, publications, selected work
Case comparison, architecture, artifacts
Contribution type, linked projects, evaluation judgment
Start with work that shows data structure, AI development workflow, and NLP/LLM evaluation judgment.
Existing Python graph libraries and generic dataframe wrappers often struggle to combine memory efficiency, traversal performance, and lazy query optimization for large graph analytics.
ML experiments and deployment work often scatter metadata, records, and artifacts across tools, making reproducible local observability hard to maintain.
The product needed ML components that could classify emotional polarity and generate stylistic variations of text to support richer blog content workflows.
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.
Shapes structured, text, image, and graph-shaped data into representations that can move into modeling and operations, with graph-native modeling and infrastructure as the deepest specialization.
Turns heterogeneous data into model-ready and system-ready structures, pipelines, and graph representations.
Connects experiment, training, evaluation, deployment, inference, observability, and recovery through the AI-Driven Development Life Cycle.
Makes run records, artifacts, model behavior, and feedback paths inspectable enough to improve.
Uses NLP/LLM research experience for modeling, evaluation design, alignment awareness, and applied system judgment.
Translates language-model research into better modeling and evaluation decisions in applied systems.
Review how each project frames a problem, chooses a structure, and turns the decision into implementation.
Open PortfolioReview education, experience, publications, and projects in a conventional document format.
Open CV/ResumeMathematics (submitted)
Shows modeling judgment that connects diagnostic context and time-series signals hierarchically instead of treating data surfaces as isolated inputs.
Journal of The Korea Society of Computer and Information
Supports the portfolio claim that NLP/LLM systems should be judged through domain data structure and error distribution, not only headline accuracy.
Korea Computer Congress (KCC)
Supports the view that LLM response quality should be treated as an evaluation, reward, and policy-update system rather than a prompt-only outcome.