LLM Chatbot to support sales team
End-to-end design of a production chatbot. A RAG pipeline over a vector database cut average response time by 90% across the majority of incoming queries.
I build AI systems that operate in high-stakes, human-centered domains — particularly healthcare. My work lives at the intersection of large language models, retrieval-augmented generation, and neural network efficiency, with a constant emphasis on making these systems reliable enough to trust in production.
Before industry, I spent five years at Clemson studying how to make deep networks smaller and faster without sacrificing what makes them useful. Eight publications came out of that work.
End-to-end design of a production chatbot. A RAG pipeline over a vector database cut average response time by 90% across the majority of incoming queries.
Built an XGBoost model to prioritize case decisions based on likelihood of approval, patient risk factors, and historical claim outcomes — then embedded it inside an automated reverification pipeline using the CMS HETS API to continuously check patient insurance eligibility and benefits ahead of treatment. Eliminated manual re-checks and reduced claim denials caused by lapsed or changed coverage, a recurring cost driver across medical device companies.
Doctoral research on making deep networks dramatically smaller without losing accuracy. Pioneered genetic algorithms for dynamic pruning and quantization of DNNs, achieving significant model acceleration. Published in IEEE ICPR and seven other venues.
Python · PyTorch · TensorFlow · LangChain · XGBoost · AWS (S3, Lambda, SageMaker, CloudFormation) · MLflow · SQL/NoSQL · Node.js · HIPAA/HL7/FHIR · ICD-10
Volunteer with Arlington Neighborhood Village, providing tech support and digital literacy help to older adults in the community — troubleshooting devices, setting up accounts, and making everyday technology feel a little less daunting.
Agentic AI architectures · LLM evaluation and safety · model compression for edge inference · the intersection of clinical workflows and machine intelligence