ACM_Citations
  • France-2005

    For outstanding contributions to computing education by placing the teaching of artificial intelligence on a statistical and quantitative foundation, and for dedicated mentoring of students and junior researchers in his field. Professor Russell has made many seminal contributions to the field of artificial intelligence research. He is best known for applying rigorous mathematical and quantitative approaches to his formulation of systems that can display intelligent behavior and learn from past experiences. Mathematical logic and probabilistic methods are the cornerstones of his approach to Artificial Intelligence. Professor Russell, after his undergraduate education at Oxford University, received his Ph.D. from Stanford University in 1986. He joined the Berkeley faculty in that year, where he is now the Smith-Zadeh Professor in Engineering and leads the Center for Intelligent Systems. He has won numerous awards in his field, including the Computers and Thought award in 1995, and is a Fellow of the ACM and the American Association for Artificial Intelligence. Russell is widely known for creating a dynamic research group among his students and post doctoral researchers, many of whom have gone on to become young leaders in the field. He has supervised the research of over 45 undergraduate students, graduate students, and post docs, who now populate industry, industrial research, and university departments. Russell is an inspirational teacher, teaching both graduate and undergraduate courses in Artificial Intelligence. He has also co-taught an innovative Introduction to Theoretical Computer Science course developed with his theory colleagues at Berkeley. Perhaps Russell's most significant contribution to teaching beyond the classroom is his seminal textbook, written in partnership with Dr. Peter Norvig, and entitled Artificial Intelligence: A Modern Approach. This book brings Russell's research insights and approach into the classroom, and it has revolutionized the teaching of artificial intelligence around the world. The book is in use in 940 universities in 91 countries, and can rightly be called the premier textbook in the field.

  • USA-2003

    For contributions to AI and machine learning.

Experience
Education
Bio
He then joined the faculty of the University of California at Berkeley, where he is Professor (and formerly Chair) of Electrical Engineering and Computer Sciences and holder of the Smith-Zadeh Chair in Engineering. He is also an Adjunct Professor of Neurological Surgery at UC San Francisco and Vice-Chair of the World Economic Forum's Council on AI and Robotics. He is a recipient of the Presidential Young Investigator Award of the National Science Foundation, the IJCAI Computers and Thought Award, the World Technology Award (Policy category), the Mitchell Prize of the American Statistical Association and the International Society for Bayesian Analysis, and Outstanding Educator Awards from both ACM and AAAI. In 1998, he gave the Forsythe Memorial Lectures at Stanford University and from 2012 to 2014 he held the Chaire Blaise Pascal in Paris. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His research covers a wide range of topics in artificial intelligence including machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, multitarget tracking, computer vision, computational physiology, global seismic monitoring, and philosophical foundations. His books include "The Use of Knowledge in Analogy and Induction", "Do the Right Thing: Studies in Limited Rationality" (with Eric Wefald), and "Artificial Intelligence: A Modern Approach" (with Peter Norvig).