I am an environmental scientist and ecologist interested in using my strong quantitative experience to answer questions related to how the environment is changing. I am currently a postdoctoral fellow at the Massachusetts Institute of Technology, funded through a highly competitive NOAA Climate and Global Change Fellowship. My research utilizes remote sensing, Bayesian statistics, machine learning, high performance computing, coding (primarily in R), and ecological forecasting. Much of my research has focused on various aspects of leaf seasonality and carbon cycling in forests. Leaf seasonality (called phenology) serves as a primary ecological indicator of climate change and has numerous ecosystem and climate impacts including nutrient cycling, energy budgets, and annual primary productivity.

Before starting at MIT, I earned my PhD in Earth and the Environment from Boston University, where my dissertation was titled “Cold-deciduous broadleaf phenology: monitoring using a geostationary satellite and predicting using trigger-less dynamic models.” During my PhD, I was funded with a highly competitive National Science Foundation Graduate Research Fellowship. I earned a B.S. in Environmental Science (concentration in Water Science) from the University of Delaware, where I was funded with a full merit scholarship. I have TAed courses on environmental modeling, ecological forecasting, and general computer science.

Looking forward, I strive to use my experience in environmental science, data science, and programming to perform impactful work and solve real-world problems.