​I'm a geospatial data scientist that works where people, land, and infrastructure intersect. 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 modeling the built environment and renewable energy system performance. My work centers on building pipelines to turn  high-resolution geospatial and temporal data into products people can use for infrastructure condition and siting decisions. 

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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. I want to unlock paper and digital data silos and make them accessible through an intuitive code-free interface.