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I am a climate data scientist specializing in probabilistic machine learning and sea level.

I recently completed my PhD in Atmosphere-Ocean Science & Mathematics from the Courant Institute of Mathematical Sciences at New York University. I was advised by Laure Zanna and am affiliated with M²LInES. My thesis focused on developing probabilistic and dynamics-informed machine learning techniques for analyzing sea level variability. Prior to NYU, I earned a B.S. in Mathematics from the University of Minnesota, where I was involved with the Mathematics and Climate Research Network.

Broadly, I am interested in quantifying risk, predictability, and decision-relevant uncertainty from the climate system, through probabilistic modeling and explainable AI, as well as through the construction of empirical-dynamical machine learning models to improve forecasts.

Outside of my work, I’m a Wisconsinite, a amateur marathoner, an amateur piano player, and an avid consumer of spicy food. Welcome to my website!