I’m a third-year PhD student in computer science at UC Berkeley advised by Ben Recht.
Previously, I received my Bachelor’s in electrical engineering and computer science from MIT and my Master’s in human rights studies from Columbia University. I work on on interdisciplinary applications of computer science, from
astrophysics to history to politics.
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Note to current Berkeley undergraduate students -> We are looking to hire an undergraduate student researcher to
contribute to an exciting collaboration between UC Berkeley and NASA
Ames during the Spring 2026 semester (with possible extension to the summer). The student will extend an
existing 3D reconstruction pipeline to multispectral image data and
assist with related low-light mapping tasks. This is a paid position
funded through a contractor role with NASA Ames. For further information on the project and to fill out an application, see here. Applications are due Monday, Jan 26.
08 SPARISTY-BASED MIXED-MODE AUTOMATIC DIFFERENTIATION2020
In Fall 2020, I took MIT class 18.337 (Parallel Computing and Scientific Machine Learning) taught by Prof. Chris Rackauckas. For the final project, I implemented and analyzed several automatic differentiation techniques for nonlinear nodal analysis in Julia.
Due to their grid-like construction, nodal systems often have sparse
Jacobian matrices, and we can take advantage of this sparsity to
accelerate their computation. In my project, I implemented forward-mode
and reverse-mode automatic differentiation to construct the Jacobian of a
nodal system. I implemented a matrix coloring scheme to accelerate
sparse forward-mode automatic differentiation of the Jacobian of nodal
systems. In addition, I implement a combined sparse automatic
differentiation technique to compute a Jacobian that is mostly sparse,
with several dense rows.
My full final project report can be accessed here. The accompanying Julia script is posted on Github.