My research has focused on methods for discovering and modeling dynamical systems from data. I use approaches from machine learning for model discovery and systems identification. I am particularly interested in how machine learning can be leveraged to discover interpretable models.
Simultaneous discovery of coordinates and governing equations. My recent work focuses on the discovery of parsimonious nonlinear governing equations from high-dimensional dynamical data. We developed a flexible autoencoder framework that simultaneously identifies a set of reduced coordinates and associated dynamical model. This work is available as an arXiv preprint. Associated code can be found on github.
Sampling strategies for data-driven discovery of multiscale systems. Systems with multiscale dynamics present numerous challenges for data-driven methods. We developed a set of sampling strategies that reduce the data requirement for modeling and discovering multiscale dynamical systems from data, focusing in particular on sparse identification of nonlinear dynamics (SINDy) and Hankel alternative view of Koopman (HAVOK). This work can be found here with associated code available on github.
Dynamical modeling of brain-wide activity. Working with Eric Shea-Brown, I have collaborated with scientists at the Allen Institute for Brain Science on modeling the dynamics of whole-cortex activity during learning. We use latent variable models and regression frameworks to model and study the dynamics of widefield calcium imaging data from mice throughout the learning of a task. This work is currently in preparation for publication.