Seminar Series Archive

We shall discuss the various ways in which data science is approached by different communities, including the Statistics, Machine-Learning, and Database communities. Each presents a different viewpoint and values different outcomes. Some consequences of these approaches will be discussed. As an example, of why data science is not machine learning, we shall sketch two important algorithms not in this class: locality-sensitive hashing and approximate counting.

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. 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. Prof. Ullman was elected to the US National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, 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.