# Bio

As of fall 2017, I am at the Flatiron Institute as a research fellow in computational neuroscience.

Before that I studied at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University, where I worked with Lexing Ying. My thesis research was focused on fast algorithms for scientific computing, in particular fast linear algebra based on data-sparse representations of rank-structured matrices that arise from physical problems in 2D or 3D.

More broadly, my interests include:

- numerical linear algebra
- numerical optimization
- machine learning
- signal processing
- spectral graph theory
- data analysis
- high-performance computing

Previously, I did my undergraduate work in mathematics and electrical engineering at Tufts University, where my advisors were Scott MacLachlan and Douglas Preis.

My graduate funding was generously provided by the Department of Energy through the Computational Science Graduate Fellowship program (CSGF). Through them, I have had the opportunity to work at a number of DOE research laboratories: