Reference Letter

This page is for use by faculty or other referrers for providing a letter of reference for the CatVehicle REU Site. Please submit your letters as soon as possible, in order for us to make timely offers to students. As long as this form is open, you can submit letters on behalf of applicants.

When preparing your letter, please speak to the following aspects of your interaction with the applicant, and provide evaluation where possible regarding:

  • How long and in what capacity you have known the applicant
  • The applicant's technical abilities
  • The applicant's potential to succeed in graduate school
  • The applicant's potential to succeed in a research career
An email to send a confirmation of receipt to the student. The student WILL NOT receive a copy of your letter.
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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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