I am a research scientist at Google. My current focus is designing and analyzing optimization methods for federated learning. I actively contribute to TensorFlow Federated, Google’s open-source framework for federated learning experimentation, and Federated Research, Google’s open-source repository for federated learning research.

I received a Ph.D. in applied mathematics from the University of Wisconsin-Madison, and went on to do a postdoc with the wonderful Dimitris Papailiopoulos. In my limited free time I often foster dogs and bake. You can find recipes I am fond of in my dissertation (no, really).


I am generally interested in optimization for machine learning, especially federated learning. My current work focuses on reconciling optimization theory with practical machine learning, especially in distributed and federated settings.

Publications & Preprints






2017 and earlier