I am interested in human optimization problems, which we solve by developing software tools to improve how people learn, remember, and make decisions. I am interested in cognitively informed machine learning, which involves the development of machine learning algorithms that leverage insights from human perception and cognition. I build computer simulation models of human cognition that allow us to predict and understand behavior. I have worked in the areas of selective attention, awareness, memory, learning, executive control, decision making, and neuropsychological disorders. Using these models, we can determine the most effective means of teaching and the manner in which to best present information for human consumption. We're just starting a project to instrument smart digital textbooks to boost student learning. We also developed the Colorado Optimized Language Tutor, which helps students learn facts (e.g., foreign language vocabulary) by scheduling study to promote long-term retention. Here's a link to a recent talk on this project. I use artificial intelligence and machine learning methods to make computer systems smarter and easier to use. A past project that got some notoriety was 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 serve on advisory boards for companies that apply machine learning and pattern recognition methods to challenging real-world problems (AnswerOn, Cognilytics, Exelis Visual Information Solutions, Imagen Technologies, Open Table, Sensory)