​I'm a geospatial data scientist with a focus on the built and human environment. I hold a B.A. in Anthropology and an M.S. in Geographic Information Science, combining expertise in social science with advanced geospatial technologies. I have over 10 years of experience in U.S. federal and municipal cultural resource management, as well as 8 years in data science specializing in built environment modeling and renewable energy system performance. My work centers on high-resolution 3D spatiotemporal data and leveraging advances in data engineering to create platforms where human-environment data addresses real world challenges.

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In my current role at National Laboratory of the Rockies, I lead the reV model's distributed energy resource (DER) and distribution modeling portfolio, where I build the data architecture connecting geospatial inputs and pipelines for techno-economic and supply chain analysis. This means designing n-dimensional exclusion, characterization, and weighting structures that drive location suitability for technology specific development, then integrating time-series energy system performance data and interconnection routing — all resolved into discrete, spatially explicit supply curves and representative generation profiles. My path from anthropology to data science began with a similar problem in a different domain. Cultural resource data are scattered across inaccessible regulatory systems with no shared schema or structure, which makes it incredibly difficult to use at a scale beyond individual sites or projects. That's my throughline (so far) — building the architecture that relates fragmented, siloed data resolve into something usable.

 

Dream PhD project: I would pursue a doctoral degree with an interested advisor to develop a cloud-based cultural resource/permitting data platform with a NoSQL backend, designed to unlock paper and digital data silos and make them accessible through an intuitive, code-free interface.