Undergraduate Research
2026 AeroAstro REU openings include projects on modeling low-noise eVTOL trajectories for urban air mobility, lab-based remote sensing and autonomy for planetary exploration, and upcoming autonomous systems research in aerospace robotics and vehicle networks.
Check out this Instagram post highlighting our 2025 Undergraduate Program and follow aeroastro_stanford on Instagram for more student research stories!
How to Apply
Submit the 2026 AeroAstro REU application
Application Deadline: Friday, April 3, 2026
Students who are interested in participating should seek out research opportunities directly with Aero/Astro faculty. While securing a commitment with a faculty member is not required to apply, priority is given to students who have already secured a faculty match. Please look at the projects below. If you are interested in a lab but it's not listed, please reach out to the faculty as they may be interested in hosting you!
Eligibility:
Students must be current undergraduates in good standing at Stanford:
- Students must be enrolled in units while using grant funding, except during the Summer.
- Students must be in the undergraduate (not graduate) tuition group while using grant funding.
- Coterm students should read this Registrar webpage for details on when you are switched to graduate tuition.*Coterminal students on graduate billing are not eligible for VPUE stipends.
- Students may not be serving a suspension.
- Students may not be on a Leave of Absence (LOA) while using grant funding. Students who have been on LOA for 3 consecutive quarters prior to the funding period are not eligible (e.g., Autumn, Winter, and Spring for the Chappell Lougee and Major Grant).
- Student athletes should confirm the impact of any awarded stipend on their athletic eligibility by contacting the Compliance Services Office prior to submitting their application.
- Stipends, prizes, or awards to students who are receiving other forms of financial aid for any purpose are a form of financial assistance and may require adjustment to their scholarship eligibility, and/or adjustment to their overall cost of attendance. The Financial Aid Office has the responsibility to determine whether adjustments are necessary and it is the student's individual responsibility to contact the Financial Aid Office about the impact of any awarded grant to their overall cost of attendance.
Room, board, house dues, and other academic expenses are paid by the student. Students are responsible for paying their university summer bill, including any other academic expenses incurred. Please consult the housing office for more information regarding rates and summer housing deadlines.
REU 2026 Projects
Aerospace Design Laboratory (ADL)
Project title: "Modeling of eVTOLs for Noise Optimal Trajectory Planning." Description: Over the course of the past decade, Urban Air Mobility (UAM) has matured from a promising new technology to a situation where multiple U.S. companies are flight testing mature designs and planning 2026 deployments. Limitations to at-scale deployment are likely to result from noise impacts (through restrictions on the daily number and times of operations as well as through public perception). We will investigate the development of a high-fidelity, near-real-time simulation framework for acoustic optimization of UAM vehicle operations. We will generate a dataset of medium-fidelity data to inform modeling strategies that can be used for low-noise trajectory optimization, based on advanced physics-based AI/ML architectures for transient flow predictions. By exploiting redundant actuation degrees of freedom in modern UAM vehicles, the work will enable the optimization of their flight trajectories into and out of realistic vertiports in urban environments, where one can mask the UAM noise footprint with the existing ambient sound level to the largest extent possible.
Preferred skills/background: Interest in aircraft analysis and design; programming experience; introductory ML knowledge.AA102 and AA141, python, and CAD.
REU Lab Openings: 2
Contact: Professor Juan Alonso for more information about applying.
Aerospace Planetary Exploration Laboratory (APEX)
Project title: "Remote sensing in a lab controlled environment." Description: Objective 1:Enable high-fidelity shape reconstruction and terrain modeling through controlled experiments, supporting the development of error budgets and validation of photogrammetry and shape-from-shading methods for mission-critical parameters like surface slope, roughness, and surface hazards. Objective 2:Autonomy for planetary exploration—such as terrain-relative navigation (TRN), onboard localization, and hazard avoidance—by emulating visual and lighting conditions in a controlled test environment. Objective 3:Autonomous navigation systems using configurable planetary analog terrains, real-time tracking, and varied lighting scenarios to iteratively improve machine learning-based autonomy algorithms and assess localization accuracy against ground-truth data.
Preferred skills/background: Matlab, C, python, experience with cameras and robotics.
REU Lab Openings: 4+
Contact: Professor Anton Ermakov for more information about applying.
Autonomous Systems Lab
The Autonomous Systems Lab (ASL) develops methodologies for the analysis, design and control of autonomous systems, with a particular emphasis on large-scale robotic networks and autonomous aerospace vehicles. Project title: "Foundation Models for Robot Autonomy." Description: Develop foundation models for robot autonomy.
Preferred skills/background: python, experience with programming and robotics.
REU Lab Openings: 2
Contact: Professor Marco Pavone for more information about applying.
Morphing Space Structures Lab
Project title: "Packaging and deployment system for a scale model of a telescope in a Lunar crater." Description: The Lunar Crater Radio Telescope (LCRT) is a project to put a 400 meter-diameter radio telescope in a crater on the far side of the Moon. We have built a 1/200-scale functional model of the LCRT parabolic radio reflector for ground testing. This scale model is 2 m in diameter and has an accuracy of 2 mm rms. We need to design a system to package and then deploy this scale model in the desert at the Owens Valley Radio Observatory (OVRO).
Preferred skills/background: Fabrication, CAD, 3D printing, rapid prototyping
REU Lab Openings: 2
Contact: Professor Manan Arya for more information about applying.
Plasma Dynamics Modeling Laboratory (PDML)
The Plasma Dynamics Modeling Laboratory (PDML) focuses on development of computational methods and theoretical models to understand physical phenomena in various plasma discharge and gas flows, with a particular emphasis on spacecraft electric propulsion. Project title: "Computational plasma modeling for spacecraft electric propulsion." Description: We develop new computational models for the plasma flows in spacecraft electric propulsion devices.
Preferred skills/background: Mathematical and analytical skills. Interests in physics. Some experience with basic coding can help.
REU Lab Openings: 1
Contact: Professor Ken Hara for more information about applying.
Reconfigurable and Active Structures (ReAct) Lab
The Reconfigurable and Active Structures (ReAct) Lab investigates the coupling between shape and function in space systems to create multi-functional adaptive structures and scientific instruments. 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.
Preferred skills/background: AA 151 is preferred; prior experience with mechanical design is helpful
REU Lab Openings: 2
Contact: Professor Maria Sakovsky for more information about applying.
Stanford Safe and Intelligent Autonomy Lab (SIA)
Project 1 description: Behavior cloning (BC) is a widely-used approach in imitation learning, where a robot learns a control policy by observing an expert supervisor. However, the learned policy can make errors and might lead to safety violations, which limits their utility in safety-critical robotics applications. While prior works have tried improving a BC policy via additional real or synthetic action labels, adversarial training, or runtime filtering, none of them explicitly focus on reducing the BC policy's safety violations during training time. We will develop SAFE-GIL, a design-time method to learn safety-aware behavior cloning policies. SAFE-GIL deliberately injects adversarial disturbance in the system during data collection to guide the expert towards safety-critical states. This disturbance injection simulates potential policy errors that the system might encounter during the test time. By ensuring that training more closely replicates expert behavior in safety-critical states, our approach results in safer policies despite policy errors during the test time. We will further develop an MPC-based method to compute this adversarial disturbance. We compare SAFE-GIL with various behavior cloning techniques and online safety-filtering methods in three domains: autonomous ground navigation, aircraft taxiing, and aerial navigation on a quadrotor testbed. Our method demonstrates a significant reduction in safety failures, particularly in low data regimes where the likelihood of learning errors, and therefore safety violations, is higher. See our website here: https://y-u-c.github.io/safegil/
Project 2 description: Autonomous systems, such as self-driving cars and drones, have made significant strides in recent years by leveraging visual inputs and machine learning for decision-making and control. Despite their impressive performance, these vision-based controllers can make erroneous predictions when faced with novel or out-of-distribution inputs. Such errors can cascade into catastrophic system failures and compromise system safety. In this work, we will compute Neural Reachable Tubes, which act as parameterized approximations of Backward Reachable Tubes to stress-test the vision-based controllers and mine their failure modes. The identified failures are then used to enhance the system safety through both offline and online methods. The online approach involves training a classifier as a run-time failure monitor to detect closed-loop, system-level failures, subsequently triggering a fallback controller that robustly handles these detected failures to preserve system safety. For the offline approach, we improve the original controller via incremental training using a carefully augmented failure dataset, resulting in a more robust controller that is resistant to the known failure modes. In either approach, the system is safeguarded against shortcomings that transcend the vision-based controller and pertain to the closed-loop safety of the overall system. We validate the proposed approaches on an autonomous aircraft taxiing task that involves using a vision-based controller to guide the aircraft towards the centerline of the runway. Our preliminary results show the efficacy of the proposed algorithms in identifying and handling system-level failures, outperforming methods that rely on controller prediction error or uncertainty quantification for identifying system failures.
Preferred skills/background: Background in robotics/autonomy. Experience with deep learning. Good coding skills, especially in Python, PyTorch
REU Lab Openings: 2
Contact: Professor Somil Bansal for more information about applying.
Structures And Composites Laboratory (SACL)
Project 1 description: Over the past few years, we created Fly-by-Feel: A framework to give aircraft situational awareness like avian (birds) from our sensor network data. We've successfully demonstrated the effectiveness of this system in wind tunnel testing (see details on our AIAA SciTech paper: https://arc.aiaa.org/doi/abs/10.2514/6.2024-2403). In the REU project, the student is expected to work on hardware and software components to make various components of this system flight-ready.
Preferred skills/background: CS-106A level programming. Basic electronics (soldering, wire splicing, etc.) and mechanical (3D printing, laser cutting) prototyping skills.
REU Lab Openings: 1
Contact: Postdoctoral Scholar Dr. Tanay Topac for more information about applying.
Project 2 description: We’re building Multifunctional Energy Storage Composites (MESC) that embed Li-ion cells into carbon-fiber structures so airframes carry structure that also stores energy. The student will be focusing on characterizing failure modes of structural batteries under mechanical loading and normal battery use, and on building and testing prototype structural-battery components sized for UAV integration (e.g., demo box-beams/panels with embedded cells and sensing). Students will assist with test operations, instrumentation, data reduction, and post-test inspections.
Preferred skills/background: MATLAB; strong structures foundation (stress analysis, fatigue, basic FEA in Abaqus); PZT/structural health monitoring experience; mechanical prototyping (3D printing/laser cutting/basic machining); familiarity with composites and battery fundamentals; careful lab documentation.
REU Lab Openings: 1
Contact: Graduate student researcher Jerry Liu for more information about applying.