I am an Environmental and Geospatial Data Scientist passionate about using data-driven approaches to better understand how human activity impacts our planet’s ecosystems. My work bridges environmental science, remote sensing, and advanced statistical modeling to uncover patterns in how Earth’s systems respond to change—and how we can use that insight to protect them.

I earned my Ph.D. in Earth and Environment from Boston University, where I developed novel Bayesian models and automated workflows for analyzing 30 TB of satellite data to predict forest leaf phenology. As a NOAA Climate and Global Change Fellow at MIT, I expanded this work globally by integrating LiDAR, eddy covariance, and MODIS data to assess various impacts to the carbon cycle.

Currently, at Industrial Economics, Inc., I apply my expertise in spatial and temporal data analysis, ecological modeling, and machine learning to real-world environmental challenges, including assessing contaminant impacts at Superfund sites and guiding natural resource restoration decisions. Across my career, I’ve combined technical rigor (R, Python, GIS, Bayesian and machine learning models) with environmental purpose—from forecasting ecosystem dynamics to helping clients interpret complex spatial data. I believe that actionable, transparent, and reproducible environmental data science is key to addressing the climate and ecological crises of our time.

Specialties: Environmental and geospatial data science, remote sensing, forest carbon, ecological forecasting, spatial analysis, Bayesian modeling, machine learning, and high-performance computing