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Announcements of Opportunity

SURF: Announcements of Opportunity

Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program.

Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.

Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor. Click here for more tips on finding a mentor.

Announcements for external summer programs are listed here.

New for 2021: Students applying for JPL projects should complete a SURF@JPL application instead of a "regular" SURF application.

Students pursuing opportunities at JPL must be
U.S. citizens or U.S. permanent residents.

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Project:  Random Tessellation Priors in Bayesian Inverse Problems
Disciplines:  Applied and Computational Mathematics, Data Science
Mentor:  Venkat Chandrasekaran, Professor, (EAS), venkatc@caltech.edu
Mentor URL:  http://users.cms.caltech.edu/~venkatc/index.html  (opens in new window)
AO Contact:  Eliza O'Reilly, eoreilly@caltech.edu
Background:  In the Bayesian framework for estimating a parameter from a set of noisy measurements, it is assumed the parameter is a random variable with a prior distribution that incorporates our beliefs about how likely the solution is to have a certain form. A posterior distribution for the parameter is then obtained by conditioning on the observations. If the parameter lies in an infinite dimensional space, the most common prior is a Gaussian process but they are often insufficient.
It is then crucial to develop and analyze non-Gaussian priors from both the theoretical and algorithmic perspectives.
Description:  Random tessellations provide a model for random piecewise constant functions that may be desirable priors in certain situations. The goal of this project will be to implement and analyze methods to sample the posterior distributions of certain tessellation priors with applications in subsurface flow models and imaging.
References:  https://epubs.siam.org/doi/10.1137/16M1096372
Student Requirements:  This project is best suited for a sophomore or more senior student with programming knowledge in python or MATLAB as well as a good understanding of numerical linear algebra.
Programs:  This AO can be done under the following programs:

  Program    Available To
       SURF    Caltech students only 

Click on a program name for program info and application requirements.

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