Data Science | Applied AI | Mining & Geospatial Analytics | Healthcare Analytics
Medical Technologist specializing in MRI, with a bachelor's-level degree in Applied Engineering (Informatics) and currently completing a Master's program in Data Science at Universidad de Santiago de Chile (USACH).
I develop applied analytics projects that combine domain knowledge, statistical reasoning, machine learning, geospatial analysis, data engineering, and reproducible software practices.
My current public portfolio spans GeoAI and mining analytics and healthcare analytics, with additional applied data science projects being progressively curated for publication.
A reproducible laboratory portfolio applying geospatial data science, spatial validation, compositional data analysis, machine learning, and GeoAI methods to mineral exploration problems.
Current public milestone: v0.2.0
Completed
- LABORATORY 00 - Engineering & GeoAI Environment
- LABORATORY 02 - Spatial Exploratory Data Analysis
- LABORATORY 03 - Spatial Cross-Validation
- LABORATORY 04 - Geochemical Compositional Data Analysis
- LABORATORY 05 - Mineral Prospectivity Mapping
In progress
- LABORATORY 01 - Geospatial Python Foundations
- LABORATORY 01A - Coordinate Foundations: completed and validated
- LABORATORY 01B - Cross-Zone CRS Analysis: completed and validated
Planned
- LABORATORY 06 - Explainable GeoAI
- LABORATORY 07 - Uncertainty Quantification
- LABORATORY 08 - Remote Sensing
- LABORATORY 09 - Deep Learning for GeoAI
- LABORATORY 10 - Graph & Multimodal GeoAI
The v0.2.0 project checkpoint includes 163 passing automated tests, signed milestone commits, a signed release tag, pre-commit quality controls, and full-history secret scanning with Gitleaks.
LABORATORY 05 demonstrates a leakage-aware mineral prospectivity workflow using spatial cross-validation, pooled out-of-fold evaluation, out-of-sample ranking, and a sealed hidden-oracle evaluation.
Under the frozen synthetic experiment, the selected prospectivity model achieved useful hidden-deposit enrichment while prioritizing substantially less than the complete candidate area.
A reproducible public healthcare analytics portfolio project using synthetic radiology workflow data to demonstrate RIS/PACS operational analytics, reporting latency, workflow analysis, data quality, management-oriented visualization, and cross-platform software quality controls.
The live dashboard is deployed on Streamlit Community Cloud and contains synthetic data only.
Data Science
Python | R | pandas | Polars | NumPy | scikit-learn | Jupyter
Geospatial Analytics
GeoPandas | Shapely | PyProj | Rasterio
Engineering & Quality
Git | GitHub | Conda | pytest | pre-commit | GitHub Actions | Gitleaks
Data & Analytics Engineering
SQL | DuckDB | data pipelines | analytical validation | reproducible workflows
Spatial analysis, geospatial data engineering, spatial machine learning, compositional geochemistry, mineral prospectivity, and reproducible GeoAI workflows.
Applied analytics for medical imaging, operational performance, RIS/PACS systems, and healthcare information workflows, informed by professional experience in MRI and medical imaging.
Statistical modelling, machine learning, reproducible experimentation, analytical pipelines, and interpretable model development across applied domains.
I focus on analytical work that is reproducible, auditable, and scientifically defensible.
This includes:
- validating assumptions before increasing model complexity;
- preventing data leakage, including spatial leakage;
- separating exploratory analysis from reusable analytical components;
- comparing advanced methods against interpretable baselines;
- using automated testing and explicit quality gates where appropriate;
- maintaining traceable version control and signed milestones;
- validating important changes through pull requests;
- communicating uncertainty, limitations, and methodological assumptions explicitly.
The next planned GeoAI laboratory is LABORATORY 06 - Explainable GeoAI.
Additional public projects in machine learning and applied data science will be added progressively after methodological, security, and publication review.