GIS and geospatial data professional building reproducible, end-to-end spatial workflows — satellite remote sensing in Google Earth Engine, spatial SQL in PostGIS, analysis in Python and R, and interactive web maps. Applied across environment, water resources, and planning.
29.8833° N, 97.9414° W — San Marcos, Texas
M.S. Geography · GPA 3.84
Where field surveying, remote sensing, and statistics meet a command line.
I work at the intersection of GIS, remote sensing, and spatial data science, building complete pipelines from raw satellite imagery and field surveys through spatial databases to analysis and web delivery. My background spans Google Earth Engine, PostGIS, Python, and R, applied to hydroclimatic analysis, suitability and flood-risk modeling, and remote sensing.
What sets my work apart is reproducibility: I favor documented code, real data, and results one can verify over a map that only looks good. I'm looking for GIS Analyst, GIS Developer, and geospatial data science roles in environment, water resources, energy, and planning.
Grouped by what they do. Emphasized items are the ones I lead with.
Reproducible work with code, real data, and results — open the repos and check.
A Random Forest model of utility-scale solar siting across Texas shows grid access outweighs sunshine by ~9×. Transferred to North Carolina, which built 4× more solar on worse sun, it localizes where siting is policy-driven, not physics-driven. Spatial-CV ROC 0.92.
An interactive scrollytelling map of summer land surface temperature across all 65 Austin neighborhoods, measured in Google Earth Engine and joined to census income via a PostGIS spatial join. Canopy–heat r = −0.87.
The same rooftop-solar method on 1 m airborne LiDAR (Austin) and a 30 m open DSM (Kathmandu), quantifying how elevation-data resolution systematically biases solar estimates.
A Markov chain–Cellular Automata model that learns 2010–2020 land-cover transitions and forecasts where San Antonio will grow by 2030 (Figure of Merit 0.85).
A custom ArcPy tool combining demographic need with transit service areas to find high-need neighborhoods underserved by bus stops in Portland, OR.
How vegetation cools the land surface across five land-cover types in Texas Hill Country, with a full reproducible R workflow (Kruskal–Wallis, ANCOVA).
Pre/post-fire change detection of the 2025 Pacific Palisades Fire using Wyvern hyperspectral imagery and red-edge vegetation indices.
From municipal GIS in Nepal to hydroclimatic research in Texas.
Hydroclimatic data analysis in Python and R; long-term climate and water-resources research; building reproducible data-processing workflows.
GNSS and UAV surveys for river, terrain, and surface-water analysis; multi-temporal remote sensing of Fewa Lake water-extent change for flood-risk management.
Multi-criteria land-use suitability analysis; wildfire-risk model for Banke National Park; cadastral/DGPS land pooling; municipal thematic mapping for planning and reporting.
M.S. Geography, Texas State · B.S. Geomatics Engineering, Tribhuvan University.
Open to new roles
Open to GIS Analyst, GIS Developer, and geospatial data science roles in environment, water resources, energy, and planning. Email is the fastest way to reach me.