My research has two complementary threads: (1) human optimization, which involves the development of software tools to improve how people learn, remember, and make decisions, and (2) cognitively informed machine learning, which involves the development of machine learning algorithms that leverage insights from human perception and cognition. These two threads often inform one another via computer simulation models of human cognition that allow us to characterize and predict behavior. Using these models, one can determine the most effective means of teaching and the manner in which to best present information for human consumption. I'm just starting a project to instrument smart digital textbooks to boost student learning. Models of cognition can also suggest new architectures for building intelligent machines.
human optimization, cognitively informed machine learning, computational models of human cognition, applications of machine learning to problems in engineering, developing tools and techniques that improve human learning, retention, performance