Jeffrey David Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. E. degree in Engineering Mathematics from Columbia in 1963 and the PhD in Electrical Engineering from Princeton in 1966. Prior to his appointment at Stanford in 1979, he was a member of the technical staff of Bell Laboratories from 1966-1969, and on the faculty of Princeton University between 1969 and 1979. From 1990-1994, he was chair of the Stanford Computer Science Department. He was elected to the US National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, the National Academy of Sciences is 2020, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), the IEEE von Neumann medal (2010), and the NEC C&C Foundation Prize (2017). He is the author of 16 books, including books on database systems, data mining, compilers, automata theory, and algorithms.

Research Interests

Currently retired, Jeffrey D. Ullman has worked on a number of the central theoretical issues in Computer Science. In the 1970's, he looked at algorithms for some of the most fundamental computing operations, such as sorting, and applied language theory to design of parsers for programming languages. His ideas formed the basis for the parser-generator YACC, which for many years was the tool of choice for building parsers for programming languages. In the 1980's he investigated the theory of relational databases. He pioneered the use of the Datalog form of logic, which has recently been used for a variety of purposes, including a commercial data-management system. In the 1990's he was involved in various database-related endeavors, including information integration. After retiring in 2002, he helped develop Gradiance, an on-line service for automating homework in a way that gets students to solve problems rather than guessing. He also developed a theory of algorithms for the modern "MapReduce" style of parallel programming. In addition, he has written widely used texts in automata theory, algorithms, compilers, database systems, and data science.

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Section 34: Computer and Information Sciences