Industrial Engineering · Forecasting · Optimization · Decision Support
I build AI-powered decision systems for complex, real-world environments. My work combines industrial engineering, data science, and software delivery to turn operational and market data into clear, auditable actions—with a particular focus on energy, industrial operations, and investment analysis.
| 01 | 02 | 03 |
|---|---|---|
| Frame the system Decisions · constraints · outcomes |
Model the uncertainty Data · forecasts · scenarios |
Ship the decision APIs · dashboards · workflows |
| I want to... | Go to... |
|---|---|
| See the flagship AI system | GridWise AI — forecasting to constrained scheduling |
| Inspect transparent investment modeling | AtlasRE — downside-first screening |
| Review forecasting and sentiment experiments | Tesla Stock Analysis |
| Choose the next GridWise direction | Answer the research question |
| Contribute an adapter | See the good-first-issue proposal |
| Review a versioned build | Read the v0.1.0 release |
| Read the publishing strategy | GitHub Audience Content Kit |
|
Energy intelligence that connects multi-horizon demand forecasting to scenario-based, constrained operating schedules. Signal: 62.0 Wh RMSE · 0.53 R² on a chronological holdout · 100 Wh peak-reduction scenario Python · scikit-learn · SciPy · FastAPI · Docker · Streamlit · CI |
Transparent investment screening for real-estate decisions, designed around downside-first analysis. Signal: Cash-flow modeling · debt constraints · LP/GP waterfalls · IRR/NPV · Monte Carlo risk Python · Streamlit · Financial modeling · Underwriting |
|
Reproducible research exploring historical market behavior through technical, sentiment, and machine-learning signals. Signal: Feature engineering · XGBoost · LSTM · time-series experiments Python · Jupyter · pandas · NumPy · XGBoost |
Energy-market intelligence, oil & gas analytics, automation, and content workflows are also part of my current work. Signal: Proprietary data and active deployments stay private; public repositories show the engineering approach. Automation · APIs · analytics · operational workflows |
I am extending this work through focused contributions to Python, forecasting, scientific computing, energy systems, and practical ML infrastructure. I prefer changes that are small enough to review and strong enough to keep:
- clear problem framing and explicit assumptions;
- reproducible tests and honest evaluation;
- maintainable APIs and documentation;
- regression coverage for edge cases and failure paths.
What I work on
- Explainable forecasting and optimization for energy and industrial applications
- Scenario analysis, constrained planning, and uncertainty-aware decision support
- Process analytics and operational improvement
- Reproducible machine-learning workflows and usable data products
How I build
Understand the system. Make assumptions explicit. Build the simplest useful solution. Measure the result.
I value clear problem framing, trustworthy data, honest evaluation, and maintainable implementation. A model is only useful when it improves the decision around it.
I am open to thoughtful collaboration on applied AI, energy intelligence, forecasting, optimization, and transparent decision-support systems.
- Start a technical conversation: GridWise AI discussions and issues
- Review a decision-support system: AtlasRE
- Explore the full portfolio: all repositories
Please include a concrete use case, data boundary, metric, or reproducible example when opening a technical issue.