Judea Pearl is a professor of computer science at the University of California, Los Angeles, where he was director of the Cognitive Systems Laboratory.  Before joining UCLA in 1970, he was at RCA Research Laboratories, working on superconductive parametric and storage devices.  Previously, he was engaged in advanced memory systems at Electronic Memories, Inc.  Pearl is a graduate of the Technion, the Israel Institute of Technology, with a Bachelor of Science degree in Electrical Engineering.  In 1965, he received a Master’s degree in Physics from Rutgers University, and in the same year was awarded a Ph.D. degree in Electrical Engineering from the Polytechnic Institute of Brooklyn. 

Among his many awards, Pearl is the recipient of the 2012 Harvey Prize in Science and Technology from the Technion, and the 2008 Benjamin Franklin Medal in Computers and Cognitive Science from the Franklin Institute.  He was presented with the 2003 Allen Newell Award from ACM and the AAAI (Association for the Advancement of Artificial Intelligence). His groundbreaking book on causality, Causality: Models, Reasoning, and Inference, won the 2001 Lakatos Award from the London School of Economics and Political Science “for an outstanding significant contribution to the philosophy of science.”   

Pearl is a member of the National Academy of Engineering and a Fellow of AAAI and the Institute for Electrical and Electronic Engineers (IEEE). He is President of the Daniel Pearl Foundation www.danielpearl.org named after his son.

Judea Pearl's work has transformed artificial intelligence (AI) by creating a representational and computational foundation for the processing of information under uncertainty（不确定性信息处理：AI演进的关键问题）.  Pearl's work went beyond both the logic-based theoretical orientation of AI and its rule-based technology for expert systems.  He identified uncertainty as a core problem faced by intelligent systems and developed an algorithmic interpretation of probability theory as an effective foundation for the representation and acquisition of knowledge（知识表示与获取）.

Focusing on conditional independence as an organizing principle for capturing structural aspects of probability distributions, Pearl showed how graph theory can be used to characterize conditional independence, and invented message-passing algorithms that exploit graphical structure to perform probabilistic reasoning effectively.  This breakthrough has had major impact on a wide variety of fields where the restriction to simplified models had severely limited the scope of probabilistic methods; examples include natural language processing, speech processing, computer vision, robotics, computational biology, and error-control coding.

Equally significant is Pearl's work on causal reasoning, where he developed a graph-based calculus of interventions that makes it possible to derive causal knowledge from the combined effects of actions and observations. This work has been transformative within AI and computer science, and has had major impact on allied disciplines of economics, philosophy, psychology, sociology, and statistics.