AI Engineer exploring the intersection of Healthcare, Law, and Biology. I build traceable agentic workflows, not just chatboxes.
Previously a Web Developer, now orchestrating Multi-Agent Systems to solve domain-specific problems.
I specialize in converting unstructured "messy" data (Medical guidelines, Legal codes, Protein structures) into computable & searchable knowledge graphs.
- Current Focus: GraphRAG, Multi-Agent Orchestration (LangGraph), Evaluation Pipelines.
- Next Move: Optimizing AI infrastructure with Rust & Deep diving into Bio-informatics (Protein Folding).
- Core: Python, PyTorch, FastAPI
- Agent & Search: LangChain/LangGraph, Neo4j, Qdrant, OpenSearch
- Learning: Rust (for high-performance serving), Flow Matching
AI-Powered Medical Insurance Review Agent
A commercial RAG solution addressing the complexity of Korean medical insurance standards.
- The Challenge: Automating reviews based on constantly changing government notices (고시) and complex calculation rules.
- My Role: Engineered the Domain-Specific RAG Pipeline handling messy regulation updates.
- Key Tech:
- Traceability: Answers include direct citations (source/clause) and calculation logic.
- Hybrid Search: Orchestrated OpenSearch (Keyword) + Qdrant (Vector) for precise retrieval.
- Impact: Presented at KHF 2025; Successfully deployed for field usage.
- Exploring Flow Matching with Transformers to optimize protein folding models (beyond AlphaFold3).
- Goal: High-throughput screening for drug discovery.
I want to push the boundaries of Claude Code. I aim to build self-maintaining subagents that can autonomously track domain knowledge changes (like new laws or research papers) and update their own testing pipelines—automating the "ResearchOps" workflow.