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
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: