An independent NLP research paper where I designed and implemented a RAG framework that performs graph-traversal over hierarchical knowledge graphs to deliver structured context to LLMs.

• Evaluated across three world scales (18-800 nodes) and four query types against a baseline using Mistral 7B
• HG-RAG achieved 1.86 factual accuracy vs. 0.02 baseline on large-scale graphs and demonstrated exceptional scaling on multi-hop reasoning (4.10 vs. 1.66 LLM judge score at large scale)

My team and I built an AI-powered transit planning web app for UC Merced students using Google Gemini 2.5 Flash to process natural language queries and recommend CatTracks bus routes.

• Integrated Google Maps Geocoding, Directions, and Distance Matrix APIs to calculate accurate bus paths and walking directions to bus stops
• Supported 9 bus routes with real-time schedule parsing and time-based trip planning given a user-specified arrival time