I'm a research software engineer working at the intersection of spatial data infrastructure and computational environmental science. I work primarily in Python, with Julia and R for modeling and research tasks.

What I build

• Geospatial data pipelines: COG/GeoTIFF, NetCDF, STAC catalogs, AWS S3, rasterio, GDAL, xarray
• Async geospatial APIs: FastAPI, TiTiler, windowed raster reads at scale
• HPC workflows: SLURM array jobs, Dask, parallelized simulation pipelines
• Process-based ecological models: spatially explicit IBMs, Approximate Bayesian Computation, genetic algorithm optimization

Research background

• Coupled human and natural systems across domains: agroforestry, urban water governance, and geopolitical response to climate intervention
• Parameter estimation and optimization: Approximate Bayesian Computation, genetic algorithms
• Climate model analysis: ESM output processing, spatiotemporal aggregation, scenario comparison

Selected projects

🌍 SKoPE Geospatial Platform: Led modernization of a production paleoclimate data system: rebuilt a Python/GDAL pipeline converting multi-band GeoTIFFs into Cloud Optimized GeoTIFFs with a STAC catalog and temporally-indexed lookup table on AWS S3, redesigned an async geospatial analysis API (FastAPI + rasterio) for windowed reads and zonal statistics at scale, and led integration of a TiTiler tile-serving microservice across the full stack. [skope-api] [skope-datasets] [skopeui]

🌡️ SDM4CI Climate Intervention Analysis: Multi-stage geospatial pipeline processing CESM2-WACCM climate model output across 7 scenarios and 35 years, extending a non-geospatial heat stress model to produce raster outputs across global population grids. Python, xarray, xesmf, SLURM, Dask, Bokeh.

🌱 SpatialRust: Spatially explicit, process-based model of coffee agroforestry systems, coupling shade tree dynamics, plant physiology, and pathogen dispersal. Calibrated using Approximate Bayesian Computation at 1-million-simulation scale. Julia, SLURM. [Zenodo] [Main model repo]

Writing

📦 Containerization for Computational Models: Peer-reviewed methods article introducing Docker and containerization practices to the socio-environmental systems modeling community, accompanied by tutorial materials and GitHub Classroom workflows for researchers learning to containerize their own models. [Article]