Judea Pearl was born on September 4, 1936, in Tel Aviv, which was at that time administered under the British Mandate for Palestine. He grew up in Bnei Brak, a Biblical town his grandfather went to reestablish in 1924.
In 1956, after serving in the Israeli army and joining a Kibbutz, Judea decided to study engineering. He attended the Technion, where he met his wife, Ruth, and received a B.S. degree in Electrical Engineering in 1960.
Judea then went to the United States for graduate study, receiving an M.S. in Electronics from Newark College of Engineering in 1961, an M.S. in Physics from Rutgers University in 1965, and a Ph.D. in electrical engineering from the Polytechnic Institute of Brooklyn in the same year. The title of his Ph.D. thesis was “Vortex Theory of Superconductive Memories;” the term “Pearl vortex” has become popular among physicists to describe the type of superconducting current he studied.
He worked at RCA Research Laboratories in Princeton, New Jersey on superconductive parametric amplifiers and storage devices, and at Electronic Memories, Inc. in Hawthorne, California on advanced memory systems. Despite the apparent focus on physical devices, Pearl reports being motivated even then by potential applications to intelligent systems.
Computer Science Department. In 1976 he was promoted to full professor. In 1978 he founded the Cognitive Systems Laboratory – a title that emphasized his desire to understand human cognition. The laboratory’s research facility was Pearl’s office, on the door of which hung a permanent sign reading, “Don’t knock. Experiments in Progress.”
Pearl’s reputation in computer science was established initially not in probabilistic reasoning –a highly controversial topic at that time – but in combinatorial search. A series of journal papers beginning in 1980 culminated in the publication of the book, Heuristics: Intelligent Search Strategies for Computer Problem Solving, in 1984. This work included many new results on traditional search algorithms such as A*, and on game-playing algorithms, raising AI research to a new level of rigor and depth.
Soon after arriving at UCLA, Pearl began teaching courses on probability and decision theory, which was a rarity in computer science departments at that time. Probabilistic methods had been tried in the 1960s and found wanting; a system for estimating the probability of a disease given n possible symptoms was thought to require a set of probability parameters whose size is exponential in n. The 1970s, on the other hand, saw the rise of knowledge-based systems, based primarily on logical rules or on rules augmented with “certainty factors.”
Pearl realized that the concept of conditional independence would be the key to constructing complex probability models with polynomially many parameters and to organizing distributed probability computations. The paper “Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach” introduced probability models defined by directed acyclic graphs and derived an exact, distributed, asynchronous, linear-time inference algorithm for trees – an algorithm we now call belief propagation, the basis for turbocodes. There followed a period of remarkable creative output for Pearl, with more than 50 papers covering exact inference for general graphs, approximate inference algorithms using Markov chain Monte Carlo, conditional independence properties, learning algorithms, and more, leading up to the publication of Probabilistic Reasoning in Intelligent Systems in 1988. This monumental work combined Pearl’s philosophy, his theories of human cognition, and all his technical material into a persuasive whole that sparked a revolution in the field of artificial intelligence. Within just a few years, leading researchers from both the logical and the neural-network camps within AI had adopted a probabilistic – often called simply the modern – approach to AI.
Pearl’s Bayesian networks provided a syntax and a calculus for multivariate probability models, in much the same way that George Boole provided a syntax and a calculus for logical models. Theoretical and algorithmic questions associated with Bayesian networks form a significant part of the modern research agenda for machine learning and statistics, Their use has also permeated other areas, such as natural language processing, computer vision, robotics, computational biology, and cognitive science. As of 2012, some 50,000 publications have appeared with Bayesian networks as a primary focus.
In 2010 a Symposium was held at UCLA in Pearl’s honor, and a Festschrift was published containing papers in all the areas covered by his research.
Pearl’s outside interests include music, philosophy, and early books – particularly the great works of science throughout history, of which he possesses several first editions. Judea and Ruth Pearl had three children, Tamara, Michelle, and Daniel. Since Daniel’s kidnap and murder in Pakistan in 2002, Professor Pearl has devoted a significant fraction of his time and energy to the Daniel Pearl Foundation, which he and his wife founded to promote Daniel’s values of “uncompromised objectivity and integrity; insightful and unconventional perspective; tolerance and respect for people of all cultures; unshaken belief in the effectiveness of education and communication; and the love of music, humor, and friendship.”
Pearl will donate a major portion of the Turing Prize money to support the projects of the Daniel Pearl Foundation and another portion to promote the introduction of causal inference in statistics education.