I use machine learning models to analyze large soil spectroscopy datasets from ecosystems around the world, developing open, reproducible workflows to predict key soil properties. In parallel, I apply process-based models to simulate soil carbon transport and biogeochemical cycling in agricultural ecosystems under changing climate and land management decisions, integrating spectral, geospatial, and field data to better understand soil system dynamics.
Before joining Woodwell Climate, I investigated soil phosphorus dynamics in subtropical grasslands as part of my doctoral research. I combined field measurements, laboratory analyses, and multi-source geospatial data, analyzing them with statistical and machine-learning approaches. These experiences strengthened my skills in data harmonization, coding, and collaborative research, which now support my work at Woodwell Climate and my broader efforts to turn complex environmental data into practical insights for climate and ecosystem studies.
Outside of work, I enjoy spending unplugged time with my family. We love hiking, skiing, and camping.
