I am currently a postdoctoral research associate in Applied Mathematics at the University of Washington, working with Nathan Kutz and Steve Brunton. I work with methods for discovering and modeling complex physical systems from data, and am more generally interested in how machine learning can be used to find interpretable models and enable new scientific discoveries. To learn more about my work, check out the research page or view my latest paper here!

I received my PhD from UW Applied Math in summer 2019. In Fall 2019, I participated in the Machine Learning for Physics long program at the Institute for Pure and Applied Mathematics (IPAM) at UCLA. As a part of my postdoc, I am also working to develop an open source software package for dynamical systems model discovery.

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.