Professor Murphey's research focuses on computational methods in robot active learning, human-machine systems, and emergent phenomena, with applications in neuroscience, health science, robotics, materials science, and machine learning in physical environments. Example applications include assistive exoskeleton control, bio-inspired active sensing, robotic exploration, and software-enabled stroke rehabilitation.
See the Projects page.
Ph.D. in Control and Dynamical Systems, California Institute of Technology, 2002
B.S. in Mathematics, University of Arizona, 1997
Professor Murphey has developed the ME 314 Machine Dynamics course, focusing on the application of variational analysis to numerical simulation and design of mechanisms. This course is now the core dynamics course for all ME majors. He has additionally developed ME 454---an introduction to numerical methods in optimal control---and ME 455---a class on active learning in robotics. In 2013 he taught an online version of one of the Engineering Analysis courses as a Coursera Massive Open Online Course (MOOC) (more information on that experience can be found at Coursera class Everything is the Same: Modeling Engineered Systems). He has been a featured speaker at the National Academy of Engineering Frontiers of Engineering Education Workshop (see his blog here).
See the Publications page.