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Parameterized matrix equations. Polynomial approximation. Uncertainty quantification.

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(technical/non-technical)

For a list of my projects, codes, and papers, visit my Stanford website.

I work on approximation methods for the solution of a linear system of equations that depend on a set of independent parameters. Such parameterized matrix equations arise in computational methods for models - particularly differential equation models - with uncertain input data.

I am particularly interested in polynomial approximation, i.e. spectral, methods such as spectral Galerkin and spectral collocation techniques.

InquisitorSo, Paul, what do you do?
MeI am a Ph.D. student in computational math.
InquisitorWhat exactly is computational math? I'm terrible at math.
MeYou want the thirty-second elevator spiel that my grandma can understand?
Inquisitor Um, okay!
MeYou've heard of physics, right?
InquisitorWell, sure.
MeAnd you've heard that physics is described by equations, right?
InquisitorI suppose so.
MeMost of those equations are extremely difficult - if not impossible - to solve. Therefore, we have to use computers to find approximate solutions to those equations. It turns out that there's an entire science behind approximating the solutions to physics equations on computers. That's what I study. In particular for my research, I study what happens when you add randomness to those equations, and then you want to quantify the uncertainty in the approximate solutions.
InquisitorWow, that's way over my head.
Choose your technical prowess.