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Carnegie Mellon Computing Expert Manuel Blum Elected to the National Academy of Sciences

Manuel Blum, Carnegie Mellon University's Bruce Nelson professor of computer science, and a leader in the world of theoretical computing, has been elected to the National Academy of Sciences, one of the highest honors that can be accorded to a U.S. scientist or engineer. Blum is one of the founders of computational complexity theory, work that has also had applications to cryptography and program checking. He came to Carnegie Mellon as a visiting professor in 1999 after a distinguished career at the University of California at Berkeley where he received an A.M. Turing Award, the highest honor in computing, in 1995. He received Carnegie Mellon?s Nelson Chair in the fall of 2001. Blum?s work has developed around a single unifying theme--finding positive, practical consequences of living in a world where all computational resources are bounded. He showed that secure business transactions and pseudo-random number generation are possible because all computational devices have finite resources. Today he is working on the Completely Automated Public Turing Test, which is used by Yahoo to ensure that registrants to Web sites are humans and not robots.

Blum attended the Massachusetts Institute of Technology where he received his bachelor?s, and master?s degrees in electrical engineering in 1959 and ?61, and a doctor?s degree in mathematics in 1964. His election to the Academy brings the number of Carnegie Mellon members to seven. The others include John R. Anderson, Stephen E Fienberg, James McClelland, Dana Scott, Robert Griffiths and Lincoln Wolfenstein.

The National Academy of Sciences was founded in 1863 to advise the government on the scientific issues that frequently pervade policy decisions. The Academy and its sister organizations -- the National Academy of Engineering, the Institute of Medicine, and the National Research Council-- 0work outside the framework of government to ensure independent advice on matters of science, technology, and medicine.

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This material is based upon work supported by National Science Foundation under Grant No. 0122581.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the
National Science Foundation