Research Overview

My primary research interests are in machine learning, particularly topics at the interface of stochastic dynamical systems and modern neural networks. While there is a focus on using foundational questions to drive advances in the science and engineering of learning systems, the theory is strongly tied to implementational problems and a wide range of very practical applications.

Some central themes of interest:

  • Robustness in ML: how to leverage randomness to make ML safer and more reliable
  • Sampling & optimization in ML: how to understand and improve sampling and optimization algorithms in ML meaningfully
  • ML for sequence modeling: how to use ML to model and learn sequences, particularly those with underlying non-trivial dynamics
  • ML + dynamical systems & statistical mechanics: how to use ideas and tools from dynamical systems and statistical mechanics to understand and improve existing ML methods