I am a Research Scientist in the Center for Computational Mathematics at the Flatiron Institute in New York City.

Optimization /Machine learning

My current work sits between optimization theory and machine-learning practice, with a particular taste for simple algorithms that exploit some structure. In order, I have been obsessed with Adam, Polyak stepsizes, Muon and non-Euclidean gradient descent work.

I have also worked on variational inference, randomized numerical linear algebra, automatic differentiation, and quasi-Newton methods.