leveraging cutting-edge AI technologies such as Large Language Models (LLMs) and reinforcement learning in aerospace autonomy, such as mission planning, task allocation, path planning, and control. 

involves developing mathematical frameworks to estimate power usage during flight

 investigating methodologies for dynamics modeling, trajectory planning, and control of tethered and high-altitude airborne wind energy systems by leveraging finite-element methods, optimal control, and reinforcement learning

integrating scalable uncertainty representation in state estimation and state-of-the-art learning and reasoning solutions to enable adaptive and robust decision-making framework for autonomous systems in resource-constrained environments