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Election Year: 2014
Primary Section: 34, Computer and Information Sciences
Secondary Section: 32, Applied Mathematical Sciences
Membership Type: Member
Judea Pearl is Chancellor professor of computer science and statistics at the University of California, Los Angeles. Pearl is recognized for his work in artificial intelligence and philosophy of science, especially through the invention of Bayesian networks, a formalism that enables computers to reason with uncertainty. He is also credited with developing a calculus of causation that has algorithmitized counterfactuals and allows empirical scientists to quantify and discover cause-effect relationships from statistical data. Pearl was born in Israel, in 1936, and graduated from the Technion, Israel Institute of Technology, in 1960, with a bachelor degree in Electrical Engineering. He came to the United State in 1960 to do post graduate work and received a Master degree in Physics from Rutgers university and a PhD degree in Electrical Engineering from the Polytechnic Institute of Brooklyn (now NYU-Poly). Until 1969, he held research positions at RCA David Sarnoff Research Laboratories in Princeton, New Jersey and Electronic Memories, Inc, Hawthorne, California. Pearl has joined the faculty of UCLA in 1969, where he currently directs the Cognitive Systems Laboratory. He is a member of the National Academy of Sciences, the National Academy of Engineering, and a Founding Fellow of the American Institute of Artificial Intelligence. Pearl received the 2002 Lakatos Award from the London School of Economics, the 2008 Benjamin Franklin Medal from the Franklin Institute and the 2011 David Rumelhart Prize from the Cognitive Science Society. In 2012, he received the Technion's Harvey Prize and the ACM Alan M. Turing Award.
Judea Pearl's Cognitive Systems Laboratory conducts research in automated reasoning, causal inference and human cognition. Results of this research are summarized in three books; Heuristics: Intelligence Search Strategies (Wiley, 1984), Probabilistic Reasoning: Networks of Plausible Inference (Morgan and Kaufmann, 1988), and Causality: Models, Reasoning and Inference (Cambridge, 2000;2009), The aims of Pearl's current research are two fold. First, providing empirical researchers with tools to elicit cause effect relationships from large data, so as to guide policies, provide explanations and facilitate extrapolation from one experimental setting to another. Second, the use of counterfactual logic to explore the limits of cognition, and the algorithmization of notions such as regret, agency, free will, morality, and responsibility.