Jeffrey Ullman


     1996 SIGMOD Contributions Award


Jeff Ullman is the Stanford W. Ascherman Professor of Engineering in the Department of Computer Science at Stanford. He received the B.S. degree from Columbia University in 1963 and the PhD 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 has served as chair of the CS-GRE Examination board, Member of the ACM Council, Chair of the New York State CS Doctoral Evaluation Board, on several NSF advisory boards, and is past or present editor of several journals. He is presently a member of the Computing Research Association Board and the W3C Advisory Board.

Ullman was elected to the National Academy of Engineering in 1989 and has held Guggenheim and Einstein Fellowships. He is the 1996 winner of the Sigmod Contributions Award and the 1998 winner of the Karl V. Karlstrom Outstanding Educator Award. He is the author of 14 books, including a 2-volume series on Database Systems and a new book on the subject written jointly with Prof. Widom. Other books include widely read volumes on compilers, automata theory, and algorithms.

His research interests include information integration, warehouse design, and data mining. In the mid-1980’s he ran the NAIL project, which developed many of the fundamental ideas behind deductive databases — ideas that are now being used in a number of information-integration systems, including current work by Ullman on the Tsimmis system at Stanford. In the past several years he has worked on data-cube design, developing a method now used in at least two commercial systems for selecting views of a data cube to materialize, in order to optimize the response rate to a given mix of queries. He has also begun a project called MIDAS (Mining Data at Stanford) to address a number of problems involved with extraction of information from very large bodies of text, including the Web. Recent MIDAS achievements include a system for optimizing very broad queries that cannot be optimized by commercial DBMS’s, algorithms for inferring causality among uses of words, and improved web search.