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.
*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: | Efficient Algorithms for Bipartite Graph Alignment | ||||||||
| Disciplines: | Electrical Engineering, Computer Science, ACM | ||||||||
| Mentor: |
Daniel Cullina,
Assistant Professor, (EAS),
cullina@psu.edu, |
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| Mentor URL: | https://sites.psu.edu/cullina (opens in new window) | ||||||||
| Background: |
NOTE: This project is being offered by a Caltech alum and is open only to Caltech students. The project will be conducted at Penn State University in State College, Pennsylvania. One way to attack anonymized user data is to exploit statistical correlations between the anonymous data and other available data to uncover true user identities. Bipartite graph alignment problems appear in deanonymization scenarios where both the users and the objects have been anonymized. From an algorithmic point of view, they interpolate between the computationally easy problem of database alignment and the hard problem of general graph alignment. |
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| Description: |
The student will explore algorithms for bipartite graph alignment, implement them, and test them on both synthetic and natural data. This will help us understand the boundary between computationally easy and hard regimes for this problem. The student will start by comparing the performance of various methods and then will begin developing more sophisticated algorithms that combine these basic ideas. Depending on the student's background, there is an opportunity to work on the mathematical analysis of the accuracy of the algorithms on random data. |
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| Student Requirements: | Proficiency in probability, linear algebra, and algorithms (CS38) | ||||||||
| Programs: |
This AO can be done under the following programs:
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