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Huihai Wang
Mid-level Applied AI Engineer specializing in knowledge graphs, GraphRAG, and urban mobility
Austin, TXPhD Candidate in Urban Intelligence5 years experienceMid-LevelTransportation & LogisticsRoboticsArtificial Intelligence
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About
ML/NLP practitioner focused on knowledge-graph-based retrieval for LLM question answering, including an urban/autonomous-vehicle decision-making use case. Built a hierarchical GraphRAG + vector database system and an entity-resolution pipeline that blends spatial and semantic similarity, validated using LLM-generated synthetic datasets; uses Python tooling like RDFLib, GraphDB, OpenAI APIs, and LangChain.
Experience
Student PICenter for Smart Transportation (CST), USDOT Tier 1 University Transportation Center
Education
The University of Texas at Austindoctorate, Community and Regional Planning (2026)
Binghamton Universitymaster, Geographic Information Science (2021)
Wuhan University of Technologybachelor, Geographic Information Science (2018)
Key Strengths
- Built an urban-domain knowledge graph to improve LLM reasoning for urban/autonomous-vehicle decision-making QA
- Designed hierarchical GraphRAG + vector-store retrieval over separated KG subgraphs (roads, accidents, POIs)
- Entity resolution approach combining spatial filtering with semantic vector similarity on names/descriptions
- Created synthetic validation datasets by rewriting entities/descriptions with LLMs and adding hard negatives
- Pragmatic optimization of matching thresholds and cost/time via staged filtering (spatial + traditional matching first)
- Modular, extensible Python workflow design with ontology interfaces for future dataset expansion
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