Dr. Richard McKee is a PhD in mechanical control systems from North Carolina Agricultural and Technical State University in Greensboro, North Carolina. His doctoral research, conducted in collaboration with the US Army Research Laboratory, focused on the design, development, and implementation of a robust Long Short-Term Memory (LSTM) neural network-based controller for a commercial DC motor. The machine learning-driven controller successfully replaced a traditional PID controller and exceeded the time-domain performance requirements while maintaining robustness and stability.

From 2021 to 2023, Dr. McKee completed multiple stints with NASA's Jet Propulsion Laboratory, where he designed and developed a Graph neural network (GNN) to emulate the Ice Sheet System Model (ISSM), supporting advanced modeling and simulation efforts. He has advanced planetary science research by processing ground-based near-infrared imagery to study seasonal color variability in Jupiter's equatorial atmosphere. He conducted applied research in integrated structural health monitoring, using acoustic emission techniques to detect cracks in aircraft wings.

Beyond mechanical engineering & aerospace, Dr. McKee also has technical expertise in Machine Learning, Deep Learning, Pytorch, Keras, Tensorflow, and AI Capstone projects. He is passionate about applying skills and techniques learned to mentor students and inspire and develop the next generation of engineers and scientists.