I am a doctoral researcher working across solar photovoltaics, machine learning, thermal modeling, and data-driven energy analytics. My work focuses on making PV systems easier to monitor, explain, and improve through trustworthy data pipelines and applied AI.
- Machine learning for solar energy systems
- PV anomaly detection and performance analytics
- Thermal modeling for outdoor PV operation
- Forecasting, data quality, and decision-support workflows
- Domain-specific AI tools with provenance and citations
| Project | What it shows | Links |
|---|---|---|
| Research Portfolio | A clean portfolio for PV, ML, and data science work. | Live site, Repo |
| Trustworthy Energy RAG Chatbot | Document-grounded technical-support chatbot with provenance, citations, OCR, and fault-code lookup. | Repo |
| SolarGraph AI | LLM-powered knowledge graph exploration for PV solar energy and materials-science research. | Repo |
| PV Power Predictor | Machine-learning workflow for predicting photovoltaic power output. | Repo |
| Bible Reading Plan App | Desktop reading-plan application built with Python and PyQt6. | Repo |
Python | scikit-learn | FastAPI | Flask | Streamlit | SQL | MATLAB | Linux | Git | MLFlow
I develop research-oriented prototypes that connect solar-energy domain knowledge with machine learning, knowledge graphs, explainable analytics, and practical interfaces for scientific insight.
- Portfolio: https://portfolio-silk-two-18.vercel.app/
- LinkedIn: https://www.linkedin.com/in/goodfriendwhyte/
- GitHub: https://github.com/marblehub


