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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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.