Daniel Barber

Research Assistant Professor in Automous Systems

Education:

Ph.D. Student in Modeling and Simulation, University of Central Florida (2012)

M.S. Computer Engineering, University of Central Florida (2006)

B.S., Computer Engineering, University of Central Florida (2004)

Contact:
Phone: (407) 882-1496
E-mail: dbarber@ist.ucf.edu

Daniel Barber earned his Ph.D. degree in Modeling and Simulation at the University of Central Florida (UCF) and is currently an Research Assistant Professor in Autonomous Systems at the Institute for Simulation and Training. Dr. Barber has extensive experience in the field of Unmanned Systems, with work in intelligent systems, machine learning, human-agent collaboration, control systems, path-planning, computer vision, communication frameworks, and environment modeling.  He has designed and overseen construction of multiple autonomous systems which consistently ranked within the top five at international events sponsored by the Association for Unmanned Vehicle Systems International (AUVSI), Office of Naval Research (ONR), and the U.S. Army. Dr. Barber has also developed multiple prototype 3D simulation environments, and is the primary developer and maintainer of the Mixed Initiative Experimental (MIX) Testbed used for HRI research by multiple universities and government organizations. Dr. Barber has also developed multimodal interfaces for squad level human-robot teaming employing speech, gestures, and visual/auditory displays. In addition to development of robotic systems, simulations, and interfaces for Human Robot Interaction (HRI) like MIX, Dr. Barber has developed tools for synchronization and processing of data from multiple physiological sensors (e.g. Eye Tracking, Electrocardiogram, and Electroencephalography) within simulation and training environments. Current areas of research include: multimodal communication, intuitive user interaction devices, supervisory control of multiple vehicles, interfaces for connected and autonomous cars, decision-making, and adaptive systems using physiological sensors.