Machine Learning in Cosmochemistry
Pattern discovery in high-dimensional, multimodal datasets of isotopic, spectroscopic, and structural grain measurements.
Single-grain datasets are growing increasingly high-dimensional and multimodal — combining isotopic ratios, Raman spectra, electron microscopy, and structural information. I develop machine learning approaches for clustering, classification, and pattern discovery in these datasets, recovering known grain populations, identifying anomalies, and revealing structure that may be invisible to traditional analysis.