1045 Regent Drive 430 UCB
Boulder CO, 80309‐0430 USA
• I develop computational models to help understand the mechanisms of human cognition. I have worked in the areas of visual perception, selective attention, awareness, memory, learning, executive control, and neuropsychological disorders. • Using these models, we build software that helps individuals to learn and remember better. One current project is the Colorado Optimized Language Tutor, which helps students learn facts (e.g., foreign language vocabulary) by scheduling study to promote long-term retention. • I use artificial intelligence and machine learning methods to make computer systems smarter. Projects include the adaptive house, a control system that learns to manage energy resources (air heat, water heat, lighting, and ventilation) in an actual residence to maximize the satisfaction of the inhabitants and minimize energy consumption. • I consult for companies that do data mining (Cognilytics, AnswerOn, JD Power and Associates Web Intelligence) and pattern recognition (Sensory, Inc.)
BS, Brown University; MA, PhD, University of California, San Diego
Michael Mozer received a Ph.D. in Psychology and Cognitive Science at the University of California at San Diego in 1987. Following a postdoctoral fellowship with Geoffrey Hinton at the University of Toronto, he joined the faculty at the University of Colorado at Boulder and is presently a Professor in the Institute of Cognitive Science and the Department of Computer Science. He presently serves on the technical advisory boards of Sensory Inc. (1995-present), AnswerOn Technologies (2001-present), and Cognilytics (2010-present). He is secretary of the Neural Information Processing Systems Foundation and has formerly served as NIPS Program Chair and General Chair, and as President of the Cognitive Science Society. He is the recipient of a 1990 National Science Foundation Presidential Young Investigator Award, and has over 100 publications in machine learning and cognitive modeling. His research involves developing computational models to help understand the mechanisms of cognition, particularly in the areas of selective attention, visual perception, awareness, memory, learning, and executive control. He uses these models to build software that assists individuals in learning, remembering, and performing better. He also utilizes machine learning prediction and control techniques to solve problems in engineering, including intelligent environments, speech and language recognition systems, intelligent tutoring systems, and data mining of customer behavior.