Microbiologist turned AI Engineer. I don't just build models—I build intelligent systems that understand lab workflows, regulatory compliance, and the science that makes healthcare work.
A decade-long journey from understanding biological systems to building AI systems that transform them.
Started in the lab running assays, understanding protocols, and living the daily reality of laboratory operations. This deep domain knowledge became my foundation.
Discovered the power of data through population health research. Coordinated international studies, learned Python and R, saw how analytics could transform raw lab data into insights.
Scaled operations during COVID-19. Managed multi-million dollar portfolios, deployed LIMS across sites, delivered 50% faster turnaround times. Earned CAPM certification.
Built production ML systems for lab operations. Developed expertise in traditional ML, generative AI, and agentic systems. Created AI that understands both science AND workflows.
Bringing it all together: domain expertise + operational excellence + AI innovation = intelligent lab informatics systems that transform healthcare operations.
Multi-national hepatitis epidemiological intelligence initiative coordinating data analytics across Nigeria–Germany partnership.
Stack: R, Python, Statistical Modeling, Epidemiology
Multi-jurisdictional population health research with strategic field operations and advanced analytics across 5 local government areas.
Stack: R, Python, Statistical Modeling, Population Health
National COVID-19 scale-up across multiple sites with cross-functional LIMS/LIS deployment and 15+ person team management.
Stack: LIMS/LIS, DMAIC, Lean Six Sigma, Quality Systems
$2M NIH research portfolio with operational dashboards, workflow re-engineering, and 100% regulatory compliance.
Stack: Python, Excel, HIPAA/IRB/IACUC Compliance
Autonomous AI system for clinical decision support using multi-step reasoning. Five clinical tools with RAG architecture and vector database of medical guidelines.
Stack: LangChain, GPT-4, ChromaDB, FastAPI, Streamlit
Production system used by University at Buffalo lab. Five ML models for predictive reordering, spending forecasts, anomaly detection, and vendor optimization.
Stack: Python, Streamlit, Firebase, Scikit-learn, Pandas
Middleware for bidirectional data synchronization between LIMS and ELN. Three ML models for anomaly detection, entity extraction, and compliance scoring.
Stack: FastAPI, PostgreSQL, MongoDB, Docker, Scikit-learn, spaCy
Modern, responsive healthcare dashboard for tracking health metrics, medications, and lab results. Real-time data sync with Firebase Firestore and interactive visualizations showing health trends.
Stack: React 18, Vite, Tailwind CSS, Firebase Firestore, Recharts, React Router
Automated compliance monitoring with OCR and NLP. Five ML models achieving 94.5% accuracy for document classification, compliance scoring, and gap analysis.
Stack: Tesseract OCR, Scikit-learn, spaCy, Transformers, React
Full-stack expertise spanning laboratory science, data engineering, machine learning, and cloud infrastructure.
Available for Lab Informatics, AI Product Management, and Healthcare AI Engineering roles. Let's transform laboratory operations with intelligent automation.