Brief Biography for Richard Sutton

Brief Biography

Richard S. Sutton is a professor and iCORE chair in the department of computing science at the University of Alberta. He is a fellow of the Association for the Advancement of Artificial Intelligence and co-author of the textbook Reinforcement Learning: An Introduction from MIT Press. Before joining the University of Alberta in 2003, he worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts. He received a PhD in computer science from the University of Massachusetts in 1984 and a BA in psychology from Stanford University in 1978. Rich's research interests center on the learning problems facing a decision-maker interacting with its environment, which he sees as central to artificial intelligence. He is also interested in animal learning psychology, in connectionist networks, and generally in systems that continually improve their representations and models of the world.

or, more humbly,

Richard S. Sutton was born in Ohio, and grew up in Oak Brook, Illinois, a suburb of Chicago. He received the B.A. degree in psychology from Stanford University in 1978, and the M.S. and Ph.D. degrees in Computer Science from the University of Massachusetts in 1980 and 1984. He worked for nine years at GTE Laboratories in Waltham as principal investigator of their connectionist machine learning project, and for three years at the University of Massachusetts in Amherst as a research scientist in the computer science department. In 1998-2002 Rich worked at AT&T Labs in Florham Park, New Jersey, and since August of 2003 he has been professor and iCORE chair of computing science at the University of Alberta. He is a fellow of the Association for the Advancement of Artificial Intelligence.

Rich's research interests center on the learning problems facing a decision-maker interacting with its environment, which he sees as central to artificial intelligence. He is the author of the original paper on temporal-difference learning and, with Andrew Barto, of the textbook Reinforcement Learning: An Introduction. He is also interested in animal learning psychology, in connectionist networks, and generally in systems that continually improve their representations and models of the world.