I'm a PhD Student in Machine Learning at Georgia Tech, where I work with Dr. Chris Rozell to develop computational tools that help scientists reveal hidden structure in data from complex high-dimensional signals. My research focuses on probabilistic models and low-dimensional structure, and includes (1) manifold algorithms for interpreting non-stationary multivariate signals; (2) Bayesian approaches for learning state space models with sparse and low-dimensional structure; and (3) disentangled representation learning for controlling the behavior of generative models.

Previously, I interned at Blackstone Real Estate, where I researched deep hierarchical ensemble models for demand forecasting. I was also a research fellow in the Machine Learning Team at the National Institute of Mental Health, where I developed statistical tools for analyzing neural signals.