• Led graduate research on EMR-based nursing surveillance decision support and diagnostic classification.
• Built end-to-end modeling pipelines using KM-BERT ensembles, XGBoost, and both structured and text data.
• Explored conversational AI topics, including empathetic dialogue generation with AI-based feedback.

• Contributed to an NRF-funded clinical AI project centered on nursing surveillance decision support using EMR data.
• Implemented workflows for clinical text understanding, including keyword extraction, dependency parsing-based preprocessing, topic modeling, and similarity analysis.
• Provided interpretable results and web-based analysis interfaces for collaborative researchers.
• Achieved 92%+ diagnostic prediction accuracy in a multi-label task by ensembling text and structured data models (KM-BERT, XGBoost).

• Implemented evaluation-related code in a human-centered multimodal AI project.
• Bridged evaluation requirements with actual code and reviewable deliverables.

• Participated in an industry-led intensive program focused on cloud, CI/CD, security, and DevOps practices.
• Contributed to implementation tasks with a focus on security and DevOps roles in project-based training.

• Held multiple leadership roles in the official university programming club and served as president in 2022.
• Planned and led study groups on machine learning, big data, financial ML, and GNNs.
• Supported campus learning communities and resource sharing as part of the Notion Campus Leader program (24-2 ~ 25-1).

• Nursing surveillance required diagnosis-related classification, but key clinical signals were fragmented across heterogeneous EMR sources.
• I integrated structured EMR features and nursing text, then used a dual KM-BERT, PCA, and XGBoost stacking architecture for ICD prediction.
• The final model achieved 0.9245 accuracy and strong rare-class recall without depending on physician-centered post-hoc documents.

0.9245 Accuracy · 0.9157 Weighted F1 · Strong Rare-class Recall

• The project needed more than frontend and backend implementation; it required cloud infrastructure and platform foundations that could reliably support search, notifications, admin workflows, and ongoing operations.
• I focused on the DevSecOps and infrastructure/platform layer, organizing CI/CD, cloud deployment flow, operational setup, and security-aware service foundations for a React + Spring commerce system.
• The project demonstrated how infrastructure and platform engineering directly shape the reliability and readiness of a production-style internal commerce service.

Internal Commerce Operations · CI/CD and Cloud Infrastructure · Security and Observability

• 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.
• I framed the service as a role-aware feedback bridge and focused on role constraints, and contextual input design so the generated responses would stay relevant and scoped.
• The prototype demonstrated how structured prompting and role-specific constraints could turn LLM output into more useful, contextualized project feedback.

Hackathon Prototype · Role-Constrained Prompts · Contextual Feedback

• The product needed ML components that could classify emotional polarity and generate stylistic variations of text to support richer blog content workflows.
• I focused on the machine learning side by implementing a KoBERT-based polarity recognition model and a KoBART-based style transfer pipeline, along with dataset handling and repeated model experiments.
• The project gave the team practical AIs for blog-oriented text processing and strengthened my experience in integrating classification and generation models into a web product context.

Polarity Recognition · Style Transfer · Web AI Contribution

• The product needed ML components that could recognize user emotion and support a diary-generation workflow from daily conversation logs.
• I focused on the ML side by implementing and refining the emotion recognition and by helping connect chatbot and summarization models into the overall AI workflow.
• The project delivered an MVP-level conversational diary experience and gave me hands-on experience in integrating Korean NLP models for emotion-aware user interaction.

Emotion Recognition · Korean NLP Pipeline · MVP Contribution