About Stephanie Buchanan

I am a quantitative modeler focused on spatial and physical systems analysis.

My work centers on extracting defensible signal from high-noise datasets using statistical modeling, angular distribution analysis, and Monte Carlo simulation. I am particularly interested in complex geophysical and anomalous clustering problems where uncertainty, drift, and spatial structure must be carefully disentangled.

Before focusing on computational modeling, I spent 15+ years working directly with physical measurement systems in laboratory environments. My experience includes thermal analysis (DSC, TGA), structural analysis (XRD), chromatography and detector systems, SEM imaging, rheological measurement, and stability modeling.

That hands-on experience shapes how I approach data. I understand how physical systems behave under real conditions, how noise enters measurements, how drift accumulates, and how assumptions can distort interpretation.

Today, my work integrates:

• Spatial statistical modeling
• Monte Carlo simulation and null hypothesis testing
• Angular alignment analysis
• Signal extraction from sensor-driven systems
• Reproducible Python-based computational pipelines

My current research investigates spatial angular alignment patterns in geophysical fault systems and reported anomalous event clusters, with a strong emphasis on methodological rigor and falsifiability.

I work on a project basis with research teams and independent investigators who need structured, defensible modeling of complex datasets.