About Dr. Bashor

Marcia O’Malley received the B.S. degree in mechanical engineering from Purdue University in 1996, and the M.S. and Ph.D. degrees in mechanical engineering from Vanderbilt University in 1999 and 2001, respectively. She is currently the Stanley C. Moore Professor of Mechanical Engineering, of Computer Science, and of Electrical and Computer Engineering at Rice University and directs the Mechatronics and Haptic Interfaces Lab. She is an Adjunct Associate Professor in the Departments of Physical Medicine and Rehabilitation at both Baylor College of Medicine and the University of Texas Medical School at Houston. Additionally, she is the Director of Rehabilitation Engineering at TIRR-Memorial Hermann Hospital, and is a co-founder of Houston Medical Robotics, Inc. Her research addresses issues that arise when humans physically interact with robotic systems, with a focus on training and rehabilitation in virtual environments. She has twice received the George R. Brown Award for Superior Teaching at Rice University. O’Malley received the ONR Young Investigator award and was also a recipient of the NSF CAREER Award. She is a Fellow of the American Society of Mechanical Engineers. She currently serves as an associate editor for the IEEE Transactions on Robotics. Additionally, she is a senior associate editor for the ACM Transactions on Human Robot Interaction.

Research Areas

Marcia O’Malley’s research addresses issues that arise when humans physically interact with robotic systems, with a focus on training and rehabilitation in virtual environments. The main goal of this research is to develop and demonstrate an adaptive training algorithm based on the display of artificial force cues within a simulated environment. These cues, displayed via an arm exoskeleton haptic feedback device, will convey additional information to the trainee beyond the physical laws that govern the simulated environment, such as desired trajectories within the environment, desired exploration speeds, and suitable interaction forces during task completion. The adaptive training algorithm will tune itself based on the individual's performance.