Overview

The David E. Rumelhart Prize is awarded annually to an individual or collaborative team making a significant contemporary contribution to the theoretical foundations of human cognition. Contributions may be formal in nature: mathematical modeling of human cognitive processes, formal analysis of language and other products of human cognitive activity, and computational analyses of human cognition using symbolic or non-symbolic frameworks all fall within the scope of the award.

The prize consists of a certificate, a citation of the awardee's contribution, and a monetary award of $100,000.

The 2011 David E. Rumelhart Prize Recipient

The recipient of the eleventh David E. Rumelhart Prize is Judea Pearl. Dr. Judea Pearl has been a pioneer in developing formal models of causal reasoning and reasoning from uncertain evidence. The Bayesian networks that he has developed can be used to represent and to draw inferences from probabilistic knowledge in a highly transparent and computationally tractable fashion. They have provided an answer to a question that vexed early artificial intelligence efforts – how can digital machines based on Boolean logic ever cope with the kind of uncertainty and fuzziness that humans face every day? Dr. Pearl’s philosophically, mathematically, and computationally rigorous answer is for automated systems to learn compressed, economical encodings of an agent’s knowledge and local dependencies between variables. While conventional wisdom maintains that “You can’t derive causation from correlation,” Dr. Pearl’s research shows that one can probabilistically determine causal relations from correlations if one has many interrelated variables and one makes some minimal assumptions about how causal processes operate. Dr. Pearl’s algorithms for Bayesian networks have been applied to areas as diverse as medical diagnosis, homeland security screening, automated user assistance, genetic counseling, natural language understanding, and mapping gene expression data. Dr. Pearl has also demonstrated that the same graphical modeling tools used for reasoning with Bayesian networks can also be used to understand causality and distill causal explanations. He has fundamentally furthered our formal understanding of the conditions under which causal relationships can be inferred from data, with implications for philosophy of science, experimental design, statistics, as well as practical applications including epidemiology, economics, and product testing.

Dr. Pearl’s academic career began in electrical engineering and physics. After receiving his Ph.D. degree in Electrical Engineering from the Polytechnic Institute of Brooklyn in 1965, he worked in industry on superconductive storage devices and memory systems before joining UCLA in 1970, where he is currently Director of the Cognitive Systems Laboratory in the Department of Computer Science. During Dr. Pearl’s long and productive career, he has written over 350 publications, including three highly influential books. He has received major awards recognizing the impact of his research across a number of disciplines, including the Award for Research Excellence from the International Joint Conferences on Artificial Intelligence (1999), the Classic Paper Award from the Association for the Advancement of Artificial Intelligence (2000), the Lakatos Award for distinguished contributions to the philosophy of science (2001), the Association for Computing Machinery's Allen Newell Award for outstanding contributions to computer science (2003), and the Benjamin Franklin Medal in Computers and Cognitive Science from the Franklin Institute (2008).

Last updated 12 August 2010 by RJG