I am a PhD candidate in Applied Mathematics at the University of Washington, where I am advised by Nathan Kutz and Steve Brunton. I’m interested in methods for discovering and modeling dynamical systems from data, and more generally in how machine learning can be used to find interpretable models and enable new scientific discoveries. Check out my latest preprint here!

Before coming to UW, I received my BA in Mathematics from Dartmouth College. My undergraduate thesis, advised by Alex Barnett and Amy Gladfelter, focused on using Markov chain Monte Carlo for tracking nuclei in microscopy videos. After Dartmouth I spent three years at the Johns Hopkins University Applied Physics Laboratory, working primarily on the development of software tools for processing LIDAR sensor data.

I have been supported by the NSF Graduate Research Fellowship Program, UW Computational Neuroscience Training Program, and the Seattle ARCS Foundation.