Through a Next Gen STEM (NGS) NASA Aerospace Skilled Technical Workforce Hubs (NAS_Hub) grant award, Georgia Tech will play a central role in addressing a critical labor shortage in the booming aerospace industry and enhance the state’s role as an aerospace industry leader in the global space economy.
Beginning this fall, the three-year, $1.5 million award will establish the Southeast Space Technology & Aerospace Readiness Skills (SE-STARS) Hub. The hub will expand opportunities for training and workforce readiness in high-demand technical careers in aerospace.
The SE-STARS Hub includes the Georgia Space Grant Consortium (GSGC), Georgia Tech’s Space Research Institute (SRI), the Georgia Tech Manufacturing Institute (GTMI), the Technical College System of Georgia (TCSG), Georgia Southern University, Georgia College and Career Academies, Georgia Aerospace & Defense Alliance (GADA), and select Georgia aerospace employers to support the growing aerospace industry and NASA missions.
Aerospace products are Georgia’s No. 1 export and the state has more than 800 companies supporting aviation, manufacturing, research, maintenance, repair, and operations. Many aerospace and engineering positions rely on skilled technical workers whose training pathways do not require a four-year, or advanced degree. There are significant gaps in technical roles such as machinists, electronics technicians, test operators, and systems integrators. These shortages are driven by an aging workforce and a projected national shortfall of at least one million skilled technical workers by 2030. (National Academics of Sciences, Engineering, Medicine, 2025). The gap also stems from the rapid pace of technological advancement, where even current skills can quickly become obsolete if not constantly renewed. All this combines to create an urgent need for training to support new and sustainable aerospace career pathways.
Stephen Ruffin, interim chair of the Daniel Guggenheim School of Aerospace Engineering and GSGC director will serve as the principal investigator (PI). Jud Ready, executive director of SRI; Lori Skillings, GSGC; Hossein Taheri, Georgia Southern University; and Steven Ferguson, GTMI, will serve as co-PIs. Jeffrey McNabb, a research engineer in Georgia Tech’s Aerospace Systems Design Lab (ASDL), will serve as the program manager, leading day-to-day operations and coordinating the project team.
"This initiative strengthens Georgia's position as a leader in aerospace innovation and manufacturing while providing new opportunities for students and industries across the state," said Ruffin.
Coordination between public education systems, industry, and workforce agencies can sometimes be limited. SE-STARS will act as a "hub" to bridge these gaps, connecting Georgia Tech and its specialized research institutes with TCSG’s 22 colleges and 88 campuses to provide statewide access to training.
“Another advantage of the program is the stronger network it creates,” said Ruffin. “Each organization in the SE-STARS team has unique capabilities but together we will strengthen the technical workforce pipeline across Georgia and the broader Southeast.”
The SE-STARS team will collaborate with industry partners to develop curriculum aligned with workforce needs. “The easily ported curricula we develop will be tailored for other institutions to adopt directly, creating a consistent foundation of skills and competencies that employers can universally expect from program graduates across Georgia,” Ready said.
Students will access training through a statewide network of technical colleges serving as regional instructional nodes, which offer flexible online options and hands-on paid apprenticeships to ensure broad geographic reach.
The program also reaches K-12 and adult learners through career academies and specialized STEM labs, creating a structured, sustainable transition from early education to high-demand aerospace credentials. GTMI will support this work through its Advanced Manufacturing Pathways Program, which connects industry-informed training with pathways into high-demand advanced manufacturing careers.
“Georgia's expanding aerospace and space sectors are creating high-paying jobs and driving economic growth,” said Ruffin. “This program will help develop the skilled workforce needed to support that growth and Georgia’s competitiveness in the space economy.”
Managing these complexities is a mathematical challenge and the Control, Coordination, and Competition under Uncertainty (C3U) Lab is tackling it head-on. Led by Assistant Professor Sarah H.Q. Li, the lab develops algorithms to help satellites make decisions to avoid orbital conflicts. She‘s developed an algorithm that allows a servicing satellite to assist other satellites that find themselves spinning out of control. Li is exploring airspace management for urban mobility, as well. She specializes in multi-agent systems and in interactive decision-making among autonomous vehicles, humans, and aircraft.
Colliding with debris can end a satellite’s mission in an instant, causing significant damage or sending the spacecraft into an uncontrollable spin. With growing congestion in low Earth orbit, other objects in space can put satellites at risk, and a poorly timed maneuver, to avoid space debris or another spacecraft, can be just as dangerous as not maneuvering at all. Li’s research helps autonomous satellites reason through these encounters, coordinate with servicing satellites when needed, and decide when it’s safest to move or hold position.
“Satellite operators haven’t established methods to automate collision‑avoidance maneuvers,” Li said. “A major challenge is the significant uncertainty that space operators must manage. Uncertainty in space exceeds what ground‑based autonomous systems encounter.”
On Earth, autonomous vehicles generally know their precise location, so the problem becomes determining how far or in which direction to move. In space, position estimates can span hundreds of kilometers. Often, operators on the ground simply cannot determine whether two objects will collide. To move or not to move becomes a critical question, especially with large differences in fuel costs. Frequent moves can quickly deplete a satellite’s limited fuel. Li’s algorithm incorporates guidance used for ground‑based autonomous systems that’s modified to account for these challenges. Li’s paper presented at the American Institute of Aeronautics and Astronautics (AIAA) Astrodynamics Specialist 2025 Conference detailing this work.
Beyond collision avoidance, satellites must also survive an impact. When a satellite is hit by debris or accumulates momentum over time, it can begin spinning unpredictably. Once that happens, stabilizing the satellite becomes extremely complicated.
In a recent AIAA SciTech Forum paper, Li and her team demonstrated a momentum‑balanced contact strategy that enables a servicing satellite to safely approach and stabilize a free‑spinning object. They simulated a it equipped with a robotic arm that approaches the spinning object and matches its rotation. Once the two are synchronized, the robotic arm extends and grabs the spinning object and stabilizes its orientation.
They’re approaching this research in multiple stages. The core algorithm is already functional, so the team is building a higher‑fidelity simulation environment to validate their approach. Once they validate the algorithm through additional experiments, they will transition to physical tests using drones.
In addition, C3U is focused on urban mobility issues with shared air space. As traffic negatively impacts major cities, and supply and demand for goods increases, the airways become a major consideration for transportation in urban areas. Once more urban airways are open, much like highways, there must be a system to avoid collision and confusion.
In shared airspace, multiple drones, each traveling to their own destination, must navigate potential conflicts as they cross one another’s paths. To manage this, Li applies principles from auction theory. In this framework, drones “bid” for access to specific portions of the airspace based on vehicle-specific preferences like specific altitudes, routes and visibility.
“An auctioneer‑like mechanism, similar to an air traffic controller, evaluates these bids and assigns routes accordingly, sometimes granting a preferred path, and other times determining that no safe route is available. The system is designed to encourage each drone to bid truthfully according to its actual needs, preventing both overbidding and underbidding,” Li explained.
By ensuring truthful participation, the mechanism can allocate airspace efficiently and fairly, allowing individually optimal decisions to produce coordinated, system‑wide safety.
“Economic models typically assume a direct link between price and reward, but engineering doesn’t work that way,” Li said. “In our world, everything depends on dynamics—how vehicles move, how routes connect, and how safety constraints interact in real time. Our inputs are acceleration vectors to the drone, which controls how the drone moves through space, and the outputs are where it ends up. Only after that can we determine the true ‘reward.’ Engineering forces us to account for complex physical behavior. Part of my work is figuring out how to bridge those two worlds.”
C3U is also exploring how large language models (LLMs), advanced systems designed to understand and generate human language, can help autonomous aircraft understand human pilots. They are developing an LLM‑enabled system that can listen to real‑time radio communications and interpret pilot intentions, allowing autonomous aircraft to anticipate pilot in unpredictable environments rather than simply reacting to it.
In a recent paper Li co-authored, she examined how LLMs can predict and improve landings at airports without a control tower.
“We are looking at using LLMs to process information so autonomous systems can infer what pilots are planning,” Li explained. “By converting unstructured audio into structured content, the system allows autonomous aircraft to plan more safely.”
For Li, the project represents another step toward a future where autonomous vehicles can navigate complex, uncertain environments by understanding dynamics and the human language shaping the airspace around them.