I’m a first-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.
My research has been featured in over 100 media outlets and has been
liked and shared tens of thousands of times on social of media.
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Curriculum Vitae
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09 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.