PhD in Energy & Environment (Duke University). I build data and optimization tools for power markets, energy sustainability, and the occasional civic-tech project.
Engineer by training (electronics & automation), researcher by trade. Fifteen+ years across energy modeling, control systems at GE Aviation, and sustainability analytics.
Power-market optimization, residential energy demand modeling, and applied machine learning — turning messy real-world data into decisions.
Filter by topic — some projects include a visual walkthrough.
Scraping, OCR digitization, and ML-based vote counting of official E-14 election forms from the Registraduría — parallelized downloads with resumable logs across machines.
Implementations of classic optimization algorithms — linear programming, network flows, and heuristics — as clean, documented Jupyter notebooks.
Modeling and analysis toolkit for battery management and dispatch of grid-scale energy storage systems.
Utilities for taming messy datasets — cleaning, reshaping, validating, and documenting tabular data so downstream analysis starts from solid ground.
No projects in this topic yet — more coming.
More on github.com/h2mauricio and gitlab.com/h2mauricio
Power-markets data analysis and software to optimize operation of the PJM energy grid; PhD in Energy & Environment (Dec 2024).
Energy and water analytics across a real-estate portfolio to support ESG goals.
Residential electricity-demand model for Mexico; energy indicators for Latin America and the Caribbean.
Robust control-flow models for embedded aircraft systems.
Software to optimize grid operation using power-markets data — collection, cleaning, modeling, and testing at scale.
Bottom-up model of household electricity consumption, later applied to analyze residential demand in China with OLADE.
Notes on data, energy, project management, and business.