projects

A snapshot of the applied AI systems I am building.

Verified Medical NLP — RDF-Grounded Jamba RAG

Building a fact-checked medical question answering pipeline that combines RDF knowledge graphs, retrieval-augmented generation, and the Jamba-1.5 Mixture-of-Experts model. Every answer is backed by structured biomedical evidence, cutting hallucinations without sacrificing coverage.

What I built

  • Deterministic result_to_summary() and result_to_sources() utilities that transform SPARQL outputs into human-readable context and DocSource evidence objects.
  • Hallucination evaluation module that scores factual consistency, retrieval coverage, and hallucination rate (H) across PubMedQA, MedQA (USMLE), and Med-HALT.
  • Zero-hallucination benchmarking harness to compare RDF-grounded vs. vanilla LLM generations using ROUGE-L/F1, retrieval precision@k, and latency.

Impact

  • 36–50% reduction in hallucination rate versus baseline LLaMA-3 and RAG-only systems.
  • Consistent F1 gains (0.79 with Jamba + RDF) while keeping inference under 3.1 seconds per query.
  • Fully auditable evidence trail for each response, enabling clinical review and compliance.

Stack

  • Python, SPARQL, RDFLib, FAISS
  • Jamba-1.5, LLaMA-3-8B, retrieval-augmented pipelines
  • Datasets: PubMedQA, MedQA, Med-HALT
  • UF HiPerGator HPC (CUDA 12.1, Apptainer containers)

Gestural AI — Real-Time ASL Interpreter

Real-time American Sign Language interpreter that achieves 94 % accuracy on a 20k+ video dataset. Designed for accessibility scenarios where latency and reliability are critical.

Highlights

  • Multimodal inference pipeline combining I3D for temporal motion, ResNet for spatial cues, and MobileNet for edge deployment.
  • MediaPipe-powered pose tracking with low-latency preprocessing in OpenCV.
  • Streamlit UI and Dockerized deployment for rapid demos on Linux servers.

Impact

  • Enables live ASL-to-text interpretation for classrooms and telehealth.
  • Modular architecture ready for additional sign languages and downstream speech synthesis.

Stack

  • Python, TensorFlow, OpenCV, MediaPipe
  • Streamlit front end, Docker deployment
  • Real-time inference optimizations for GPU and CPU targets