Biomedical AI

Context At NCSA, I worked on improving reasoning and retrieval in biomedical AI systems by incorporating knowledge graphs into LLM workflows. The research focused on building a structured knowledge base of genes, phenotypes, and biomedical relationships to support causal question-answering over CRISPR perturbation data. What I Built I built a Neo4j knowledge graph with 100K+ biomedical triples by harmonizing Monarch ontology data, creating a structured layer for multi-hop reasoning over biomedical entities. ...

Context For my UIUC research project, I worked on SHERPA, a semi-structured RAG framework designed to improve retrieval and reasoning by combining unstructured biomedical text with structured knowledge graphs. Traditional RAG systems rely heavily on vector search over unstructured documents. Our goal was to explore whether adding structured memory through entity-relation extraction and knowledge graph construction could improve retrieval fidelity, reasoning, and answer quality for biomedical question answering. What I Built My contribution focused on two components: query classification and NER / relation extraction for triplet generation. ...