My research interests include sparse recovery, theoretical machine learning, signal processing, numerical linear algebra, and spectral graph theory, and especially the applications in which some subset of these paradigms intersect. I like developing provably robust, efficient algorithms for inverse problems, sometimes in imaging applications.
Here are a few questions I've been thinking about recently:
- What invariances should a message-passing neural net for multireference alignment obey?
- What's the fastest way to rotationally align two spherical functions?
- What generalizations of (1) the restricted isometry property and (2) leverage score sampling might be useful for off-grid sparse recovery?
- What practical considerations determine the real-world utility of switching-constrained online optimization algorithms?
Applied Math Departmental Student Advisory Committee, Spring 2019
Dean's Committee on Science and Quantitative Reasoning, Fall 2018
Undergraduate Learning Assistant, CS 365 (Design and Analysis of Algorithms), Spring 2018
Undergraduate Learning Assistant, CS 223 (Data Structures and Algorithms), Spring 2017
Undergraduate Learning Assistant, CS 201 (Introduction to Computer Science), Fall 2017