Frequently Asked Questions

If your question is not answered below, then you can visit the "Contact Us" link in the footer.


I already filled out the application: why am I getting emails about filling out Part 2?

Our application requires 2 pieces:

  1. The Google Form, a common REU Site application form that has a billion questions and lots of places to fill out fields and select from dropdown boxes.
  2. Part 2, which permits you to provide a statement of purpose, your unofficial transcript, and the names/emails of letter writers.

For reasons that aren't worth explaining, we have to have a 2 phase process. Unfortunately, it's not always easy to see that you need to do part 2, so we send reminders if we find a name in Part 1 that doesn't have a Part 2 submission.

If you accidentally filled out Part 1 more than once, then you will get a reminder email for the extra Part 1 submissions, unless we get lucky and pull out those submissions.


It's after the deadline, but I still want to apply. Can I still apply?

We will continue to accept new applications until the positions have been filled. All applications that are fully submitted before the deadline are guaranteed to be considered. However, if your application comes in after the deadline, we may make an offer to another student in between when you submit your application and when applications are closed. So please submit your application as soon as possible after the deadline closes, if you find yourself submitting after the deadline.


Faculty, you might get requests for letters for a few reasons: the student may be applying after the deadline because they just heard about the program. Or, we may have encouraged an applicant to apply even though the deadline has passed. In any case, please do submit your letters as soon as you can, using the URL you were mailed to do so. We will close down the reference submission form when we have filled all our positions.


We will continue to accept applications until all our positions are filled. If in doubt, go ahead and fill out an application.


You cannot graduate before the end of the CAT Vehicle program for the summer you apply. This is because we hope to prepare you for graduate school, and by the summer it is too late to apply. Instead, consider applying directly to the UA Graduate School for the chance to do research as a graduate student.


I already applied to another REU Site that used the common application. Do I still need to apply through the CAT Vehicle's process? These look like the same questions I just answered.

If you are interested in CAT Vehicle, we recommend you go ahead and fill out our application. Most of the Common Application questions will be the same (and we know this is boring). The way the common application works, you are considered in other programs only after they have evaluated their first set of applicants. Importantly for us, we need your Step 2 application, which will give us (and you) an idea of whether the CatVehicle REU and you are a good fit.

CAT Vehicle 2019

In 2019 summer, I worked on the CAT Vehicle REU at the University of Arizona. My group created a specialized language that will be used at local Tucson elementary schools to code Lego EV3 robots and the CAT Vehicle (an autonomous vehicle). I want to thank the University of Arizona, the NSF, and the other members of CAT Vehicle and HF projects.

Alex Day's experience: This video outlines the project that I was a part of during the University of Arizona's CAT Vehicle REU.

Video experience of Riley's project on the use of a domain-specific modeling language (DSML) designed in WebGME — a server-based generic modeling environment. The language mirrors the curriculum of non-expert programmers and incorporates the use of sensor data, which is to be deployed on both the Cognitive and Autonomous Test Vehicle (CATVehicle) and Lego EV3 robots. However, maintaining safety within these DSML-designed CPS can be an issue.

Autonomous driving has captured academic and public imaginations for years. This project attempts to implicitly teach a car to follow the best optimized route to a destination while avoiding obstacles. The car is taught the optimized route based on a reward/penalty system via reinforcement learning. Using only the distance away from the nearest object and the angle of said object, the car avoids collisions and learns the optimized route in computer simulated worlds.

Eric Av's Video experience: Autonomous driving has captured academic and public imaginations for years. This project attempts to implicitly teach a car to follow the best optimized route to a destination while avoiding obstacles. The car is taught the optimized route based on a reward/penalty system via reinforcement learning.

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