Dr. Kevin Haughn is an Assistant Professor of Mechanical Engineering. Kevin also volunteers as a pole vault coach for the RHIT track team. He earned his doctorate in aerospace engineering at the University of Michigan, where his research focused on using machine learning to develop controllers for autonomic morphing wings in a dynamic aerial environment. After defending his PhD, Kevin worked for the U.S. Army Research Laboratory, where his research incorporated avian-inspired morphologies and control surface design to improve small aircraft maneuverability and adaptability. View my personal/lab website.
Academic Degrees
- PhD, University of Michigan, 2023
- BS, University of Michigan, 2018
Teaching Interests
- Computational and Intelligent Controls
- Aircraft Stability and Control
- Conservation and Accounting Principles
- Structures
Research Interests
- Bio-inspired design, sensing, and control
- Morphology and maneuverability
- Reinforcement Learning
- Adaptive Structures
- Intelligent Systems
Publications & Presentations
- Haughn, K.P.T., Auletta, J.T., Hrynuk, J.T. et al. Electrostatic adhesion mitigates aerodynamic losses from gap formations in feathered wings. Commun Eng 4, 178 (2025).
- Barri, K., Haughn, K.P.T., Henry, T.C. et al. Rotational bistable mechanisms for morphing wings and beyond. Commun Eng 4, 164 (2025).
- Henry TC, Haughn KP, Morales M, Hrynuk JT. Ball-and-Socket joint produces longitudinal and lateral control with a horizontal feathered tail for small uncrewed aerial systems. International Journal of Micro Air Vehicles. (2025);17
- Haughn, K.P.T., Harvey, C. & Inman, D.J. Deep learning reduces sensor requirements for gust rejection on a small uncrewed aerial vehicle morphing wing. Commun Eng 3, 53 (2024).
- Maraj, Joshua J., et al. "Sensory adaptation in biomolecular memristors improves reservoir computing performance." Advanced Intelligent Systems 5.8 (2023): 2300049.
- Haughn, Kevin PT, Lawren L. Gamble, and Daniel J. Inman. "Deep reinforcement learning achieves multifunctional morphing airfoil control." Journal of Composite Materials 57.4 (2023): 721-736.
- Haughn, Kevin PT, and Daniel J. Inman. "Autonomous Learning in a Pseudo-Episodic Physical Environment." Journal of Intelligent & Robotic Systems 104 (2022): 32.