Research Experience for Undergraduates Program

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 24 2024 program end date will be August 30 2024 with the REU poster session being on the final week.

2024 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 April 15 2024 for summer quarter. 

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

REU Student Openings in Aero Astro Labs for Summer 2024 

Aeronautics Astronautics faculty labs, current research projects, and openings for Undergraduate students are updated here regularly. 

Professor Anton Ermakov, email: aie@stanford.edu if you are interested. 

Project Description: 

How smooth are lava lakes Jupiter’s moon Io and what does it tell us about Io’s volcanism?

On Feb 3, 2024, Juno had a flyby of Jupiter’s moon Io. Io is the most volcanically active body in the Solar System. Its surface is covered by geologically young lava flows from dozens of volcanic centers. The observations by the camera onboard Juno (JunoCam) revealed that the surface of lava lakes is mirror-like. That is, it exhibits specular (as opposed to diffuse) reflection.

Four images shown below were taken during the flyby. The horseshoe shaped feature is the largest volcanic lake on Io called Loki Patera. It can be seen that the lava lake surface, which is normally much darker than the surrounding terrain, appears brighter at the left image.

The project is to quantify the strength specular reflection and relate it to the properties of the surface. For example, the surface of lava lakes must be very smooth to be mirror like. Thus, the lava might have been of low viscosity and with no bubbles. The project will involve JunoCam image processing, literature review and numerical modeling of the specular reflection.

Prerequisites: strong programming background (Matlab, Python). It would be preferred (but not required) if you have taken Geophysics or EPS classes on anything related to volcanos.

Current REU Openings:

1-2

Morphing Space Structures Lab, Professor Manan Arya, email: manan.arya@stanford.edu if you are interested. 

Project description: The REU student will assist with the construction of small-scale prototypes of large unfolding structures for space applications, e.g., large radio reflectors or high-power solar arrays. These small-scale prototypes, measuring around 1 m in size, would be used for assessing and furthering the design of these structures. Some of the prototypes will be realized using low-fidelity materials, such as 3D-printed plastic, laser-cut sheets, and folded paper. Some of these prototypes will be realized using higher-fidelity materials to enable the experimental characterization of these prototypes.

Eligibility requirements: strong background in design, CAD, and prototyping. Knowledge of structures and structural analysis is important.

Current REU Openings:

1

Aerospace Design Laboratory (ADL), Professor Juan Alonso, email: jjalonso@stanford.edu if you are interested.

The Aerospace Design Laboratory (ADL) fosters the use of high-fidelity analysis and design tools in a variety of aerospace design problems including aircraft, turbomachinery, launch vehicles, helicopters and spacecraft. Aerospace Design Laboratory (ADL)

Current REU Openings:

1-2

Reconfigurable Structures Lab, Professor Maria Sakovsky, email: sakovsky@stanford.edu if you are interested.

Project description: Our research explores aerospace structures that can learn from inputs in their environment and change their mechanical properties on demand. Imagine a satellite solar array that passively reorients to face the sun without repointing the satellite or a robotic explorer that learns to navigate around obstacles by changing its type of locomotion. We are looking for students with experience and interest in materials and structures. Projects range from performing mechanical characterization of structures, to integrating sensors, to writing code to control the structures.

Current REU Openings:

1-2

Space Rendezvous Laboratory (SLAB), Professor Simone D'amico, email: damicos@stanford.edu 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)"

Current REU Openings:

3

Stanford Intelligent Systems Laboratory (SISL), Professor Mykel Kochenderfer,  If interested please apply at: https://forms.gle/Cboj8cJTa3JaXYG89

We are looking for an undergraduate to work on algorithms for decision making under uncertainty, applied to a variety of applications ranging from space exploration to unmanned aircraft. Programming knowledge in Julia or Python is required. Ideally, students will have taken AA228 already. 

Current REU Openings:

2

Structures And Composites Laboratory (SACL), Professor Fu-Kuo Chang. Welcome to apply! Follow the link for more information: https://sacld8.sites.stanford.edu/

 To apply, please send your CV to the Lab PI, Prof Fu-Kuo Chang, at fkchang@stanford.edu and CC to the Lab Manager, Dr. Saman Farhangdoust, at sfarhang@Stanford.edu

Join our dynamic research team at the Structures and Composites Lab (SACL) within the Aeronautics and Astronautics Department. We are looking for motivated, independent, students interested in research on developing multifunctional energy storage composites (MESC) for the next generation of electronic vehicles and aircraft.

We currently have two openings for some active projects and successful applicants will have the opportunity to get training in an academic program specialty and adopt professional skills to participate in two programs:

1. Experimental Program including Lithium-Ion Battery Fabrication, MESC Specimen Fabrication, Testing MESC Specimen (Peel, Thermal-Expansion, Fatigue, Impact, etc.).

2. Computational Program including Design and Conceptualization of MESC Applications, Hand Calculations, Finite Element Analysis, Simulation Modeling of MESC.

Two candidates with a strong background in one or more areas of computational modeling, composite fabrication, lithium-ion battery, material science, and computer programming are sought. These two positions will be placed at SACL (Aeronautics and Astronautics Department). The students will have access to workstation computers and professional lab facilities. The students will have the opportunity to conduct modeling and experimental work and collaborate with interdisciplinary researchers at SACL.

In addition to the research project, a Mentorship Program is designed to assist students in acquiring and developing academic skills under postdoctoral scholar mentorship. This mentoring program includes In-depth involvement in the research project, Interaction with industries, Training in oral presentations during group meetings, Publication of high-quality articles in journals and conferences, Guidance for career development and future paths. 

Current REU Openings:

2

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REU Projects from 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: manan.arya@stanford.edu 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: kenhara@stanford.edu 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. 

Reconfigurable Structures Lab, Professor Maria Sakovsky, email: sakovsky@stanford.edu if you are interested.

Title: Self-sensing of shape in multi-stable structures

Description:Multi-stable structures can hold multiple shapes passively, similar to the popular slap bracelet toy. Multi-stability is of interest in space structures as a way to reduce the energy usage in shape adaptive structures as power is required only to change shape but not maintain shape. Additionally, multi-stability promises to simplify control of shape change as the structures can more reliably achieve the desired configurations. For example, research at the Reconfigurable and Active Structures lab has demonstrated the application of multi-stability to robotic locomotion and reconfigurable satellite antennas. While there are many examples of multi-stable structures, there are few that can sense their current shape – a feature required for closed-loop control. This research will focus on integrating sensors into an existing multi-stable structure to demonstrate self-sensing capabilities.

Skills required:

· Understanding of lightweight structures (ex. AA151)

· Experience in lab techniques (ex. material/structural mechanical characterization, basic circuits)

Space Rendezvous Laboratory (SLAB), Professor Simone D'amico, email: damicos@stanford.edu 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: mykel@stanford.edu 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