Research Experience
My research covers knowledge-graph retrieval, computer-vision systems, and field IoT. I conducted this work through faculty-supervised undergraduate research and the government-funded IITP Korean AI Square Summer 2025 Program.
The entries distinguish my implementation and evaluation work from team results and institutional outcomes.
Research Positions
Investigated how graph-structured knowledge could support grounded multi-hop question answering, with responsibility for retrieval-pipeline, model-integration, and application-system implementation within a supervised lab environment.
- Designed and implemented a PyTorch- and LangChain-based pipeline that integrated graph relations with neural embedding retrieval for multi-hop question answering
- Implemented mechanisms for assembling connected graph evidence for questions requiring multiple factual links
- Conducted ablation studies on embedding-integration approaches to compare accuracy and computational-efficiency trade-offs
- Implemented the Flutter application, FastAPI backend, and DETR/CLIP detection pipeline for a collaborative cultural-heritage damage-assessment project
- Documented system architecture, implementation decisions, and evaluation findings for research and project materials
IITP Korean AI Square Summer 2025 Program (Government-Funded)
Contributed field research and data collection to an international team evaluating a LoRa–MQTT monitoring system for conditions inside and outside beehives.
- Supported data collection and field-test execution for a two-month trial using four sensor nodes across two beehives
- Contributed to evaluating packet delivery, transmission range, latency, uptime, and hardware cost for the team-developed system
- Worked with an international, interdisciplinary team to document the research methods and results
- Co-authored the resulting six-author arXiv preprint as third author
Education
See also: Awards · Activities