Published: April 30, 2020

NASA LogoFive students in the Ann and H.J. Smead Department of Aerospace Engineering Sciences are being recognized with 2020 NASA Space Technology Graduate Research Opportunities (NSTGRO) fellowships.

The annual program sponsors U.S. citizen and permanent resident graduate students who show significant potential to contribute to NASA’s goal of creating innovative new space technologies for our nation’s science, exploration and economic future.

NASA Space Technology Graduate Researchers perform research at their respective campuses and at NASA Centers. Awards are made in the form of training grants to universities on behalf of individuals pursuing master’s or doctoral degrees, with the student's faculty advisor serving as the principal investigator.

In addition to their faculty advisor, each awarded student is matched with a technically relevant and community-engaged NASA Subject Matter Expert, who will serve as their research collaborator. The research collaborator will serve as the conduit into the larger technical community corresponding to the student’s research area.

Read below for more information about each honoree and their research.

2020 Honorees

Aaron Allred

Advisors: Alireza Doostan and Kurt Maute
Lab: Aerospace Mechanics Research Center

My research at CU Boulder incorporates both computational material design and uncertainty quantification.  For my current research at CU, I am investigating multi-scale damage models.  Specifically, I am working on developing and implementing a multi-scale damage model for composite material systems for use in a topology optimization under uncertainty framework.  Through the NSTGRO fellowship, I plan to leverage both computational damage models and machine learning with the goal of uncovering new insights into the mechanisms governing the stochastic nature of fatigue in aerospace structures.  This proposed research aims to accelerate the production and application of new material systems and provide more capability to future space vehicle and space habitat design.

Kristen Bruchko

Advisor: Natasha Bosanac
Lab: Bosanac Research Group

Throughout my time at CU Boulder, I will be researching a new approach to constructing trajectories in multi-body systems using roadmap generation and dynamical systems theory. Designing a feasible and efficient trajectory in a chaotic environment is a complex process that is time-consuming for a human, requires an adequate initial guess, and expert knowledge of the environment. I plan to incorporate roadmap generation techniques to solve the path planning problem to autonomously choose the trajectory path and dynamical systems theory to summarize the total solution space. By combining these approaches, I will develop a more efficient method that requires less human interaction and will address common challenges in mission planning. By the end of my graduate studies, my goal is to develop a new approach that will support mission planning in cislunar and deep space via autonomous trajectory design. Ultimately, I want to help expand the capabilities of space exploration by enabling advanced missions to complex destinations.

Matthew Hardy

Advisor: Jim Nabity
Lab: Bioastronautics Laboratory

For my project, I will focus on decreasing the cost of growing higher plants in space. Right now, the lighting supplied to plants can contribute to over half of the total cost of growing them on the Space Station. While the recent introduction of efficient LED lighting has helped decrease the cost, there is still much more room for improvement. Since efficiency improvements in LEDs are starting to plateau, it is time to look elsewhere to cut the cost of providing light to plants. Through my work, I plan to improve the light emission quality. Instead of blindly projecting light over the entire growth area, I plan to scan the plants, integrate the images in a control algorithm, and supply light only to the canopy. This would help ensure that a higher fraction of emitted light is used by the plants. I hope that this will decrease the cost of producing crops, thereby decreasing the cost of sustaining human presence on and beyond Earth.

Adam Herrmann

Advisor: Hanspeter Schaub
Lab: Autonomous Vehicle Systems (AVS) Laboratory

My PhD research focuses on solving the spacecraft operations scheduling problem using both classical optimization techniques and reinforcement learning to enable spacecraft autonomy in the Earth-observing and small-body domains. I am particularly interested in bridging the gap between these two fields, exploring the trades between the two in solving complex spacecraft planning problems. Furthermore, I am interested in how these computationally intensive planning algorithms can be implemented onboard spacecraft. How do you balance computational complexity with plan robustness in uncertain environments? Can you fly a high-fidelity dynamics simulator onboard to help solve these problems? In addition to optimal spacecraft planning, I am also interested in studying how supervised learning techniques can be applied onboard spacecraft to better predict resource usage during spacecraft plan execution, reducing constraint violations and replanning efforts.

Jordan Murphy

Advisor: Daniel Scheeres
Lab: Celestial and Spaceflight Mechanics Lab (CSML)

Throughout my graduate studies, my research will be focused on the applications of Deep Learning and Reinforcement Learning techniques to astrodynamics and spacecraft trajectory design. The first part of my research will be focused on utilizing deep learning to extract and model N-body dynamics.  I'm currently looking into using a combination of Graph Neural Networks and Neural ODE's to accomplish this. After learning these dynamics, a control method, through some form of reinforcement learning, will be developed to create an autonomous mission designer. By first learning the dynamics through deep learning, it is expected that stronger algorithms for designing and predicting the reward function will be able to be developed as a result.  Additionally, it's expected that the simulation of the dynamics will be quicker than traditional methods allowing for more efficient training methods. Approaching this problem from a machine learning perspective should allow for a quicker, robust manner to generate candidate trajectories and more fully explore the design space for mission concepts.

Note: Both Bruchko and Herrmann were also selected for the 2020 National Science Foundation Graduation Research Fellowship Program. The NSF GRFP is similar to NSTGRO, and honorees can only accept one. Bruchko and Herrmann have each chosen the NSTGRO.