PRECEPT: On the Nature of Data Science
Prof. Jeffrey Ullman *66, Stanford University
Data Science is a multidisciplinary approach to aid decision-making by extracting actionable insights from vast amounts of data. Many different disciplines, including Computer Science, Statistics, and Machine Learning, often lay claim to Data Science. Interestingly, the boundary between Statistics and CS was a major issue at Princeton in the 1960's, when I was a graduate student and then a young faculty member. I'll talk a little bit about the history, but then talk about the issues and contention of today. Along the way, I'll give a few examples of why Data Science should be considered a part of CS.
Thursday, OCT 21, 2021
6:00 - 7 PM Central
Virtual Via Zoom
Link will be e-mailed to registered guests on 10/20.
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Prof. Jeff Ullman is a recipient of the 2020 ACM A. M. Turing award, which is generally recognized as the highest distinction in Computer Science and is often referred to as the “Nobel Prize of Computing”. He 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 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. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, the National Academy of Sciences in 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.