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Research Experience for Undergraduates Program

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The Aero/Astro Research Experience for Undergraduates (Aero/Astro REU) program is designed to give undergraduates the chance to work with faculty and their research groups on advanced research projects over the summer.  Students who are accepted into the program will receive a stipend for their full time research work.

 Full-time means devoting 35+ hours/week for 10 consecutive weeks, i.e., it is the student's primary activity that quarter. Program start date will be June 26 2023 program end date will be August 31 2023 with the REU poster session being on the final week.

2023 Program Updates

  • Interested students should email the faculty member they are interested in working with and submit their resume. 
  • Faculty will submit student applications for the REU program to the student services office.
  • We will offer the program in-person, & only during summer quarter.
  • We must receive all REU applications from faculty by mid April 2023 for summer quarter. 

Please read through our FAQ page for more information about the program & eligibility.

List of REU Projects for Summer 2023

 please note this list doesn't include all our REU projects. Please contact faculty directly to learn if they are offering undergraduate research projects.

Morphing Space Structures Lab, Professor Manan Arya, email: if you are interested. 

Project description: “Large-scale structures in space are often folded for launch and unfolded in space. An alternative approach is to robotically assemble or manufacture in-space these structures. This project will explore the feasibility of potential combinations of these approaches. Analytical, experimental, or numerical approaches may be adopted to address these questions. Students with skills and experience in robotics, structural mechanics, or classical dynamics will be well-suited for this research.”


Plasma Dynamics Modeling Laboratory (PDML), Professor Ken Hara, email: if you are interested.

Project description: We develop computational and theoretical models for low-temperature plasma applications, including spacecraft electric propulsion and plasma processing for semiconductors. We are looking for students with good analytical skills (particularly in fluid dynamics, electromagnetics, and plasma physics) and strong interests in developing (writing) your own code. Coding experiences in matlab, python, C/C++, Fortran, etc. would help. 


Space Rendezvous Laboratory (SLAB), Professor Simone D'amico, email: if you are interested.

Project description: "Autonomous and distributed spacecraft Guidance, Navigation, and Control (GNC) is an enabling technology for sustainable spaceflight, including on-orbit servicing to prolong the lifetime of space assets (e.g., through inspection, refueling and repair) and to remove space debris (e.g., through their characterization and de-orbiting). These projects investigate and develop new algorithms at the intersection of optimal control, computer vision and machine learning to enable the above in a spectrum of scenarios from known cooperative (on-orbit servicing) to unknown non-cooperative (debris removal) resident space objects. This research work leverages the experience and expertise of the Stanford’s Space Rendezvous Lab in the design and validation of robust algorithms for distributed space systems. The research is done in collaboration with external partners at various space companies (Blue, Redwire, TenOneSpace, etc)"


Stanford Intelligent Systems Laboratory (SISL), Professor Mykel Kochenderfer, email: if you are interested.

Project description: This REU will explore the problem of domain generalization in machine learning. In the context of autonomous driving, this could be leveraging data from different driving datasets beyond just training models on multiple datasets while hoping that this leads to a better generalization to unseen datasets. From a probabilistic standpoint, each dataset can be seen as a "meta-datapoint" (more like a distribution itself) that comes from an underlying "meta-distribution". If we could learn this underlying "meta-distribution", it could be possible to generate synthetic datasets that cover a broader spectrum of scenarios than what is present in the available datasets. Having access to this "meta-distribution" would allow us to build models that are much more robust beyond even training them on a handful of datasets where they are likely to overfit. The biggest challenge for this problem is certainly learning such a meta-distribution given that typically only a very limited number of datasets is available for each task.

Eligibility requirements:

  • Intermediate knowledge of Python and PyTorch for image processing
  • Experience with dataset pre-processing
  • Familiarity with Linux-based HPC or willingness to learn about it