My main interest is working at the intersection of subsurface data analysis and machine learning, with broad interest in geostatistics.
This profile showcases my academic and applied projects, from predictive modeling of mineral occurrences using real-world geophysical data to exploratory analysis of complex, multivariate datasets. I work mainly with open-source tools and public datasets (e.g., CPRM, ANP).
With a background in Earth sciences and quantitative modeling, my experience bridges spatial data analysis, exploratory statistics, supervised machine learning, geophysical & geological processing data and open geodata workflows.
I have worked with airborne magnetometry and gamma-ray spectrometry, borehole data, lithologies, faults, and structural features to build interpretable ML models and data visualizations.
Soon:
- Geodata Exploration Toolkit — (Coming soon)
- ML models for copper prediction using geophysical and geological data — (Coming soon)