Big-Data Algorithms That Are Not Machine Learning

We shall introduce four algorithms that run very fast on large amounts of data, although typically the answers they give are approximate rather than precise.  (1) Locality-sensitive hashing (2) Approximate counting (3) Sampling (4) Counting triangles in graphs.

About Dr. Jeff Ullman

Jeff 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 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 Science 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), the NEC C&C Foundation Prize (2017), and the ACM A.M. Turing Award (2020). He is the author of 16 books, including books on database systems, data mining, compilers, automata theory, and algorithms.

About the Robert Stewart Distinguished Lecture Series

The Robert Stewart Distinguished Lecture is made possible by the generous contribution of Dr. Long Ngyuen, who received his doctorate degree from the ISU Computer Science department in 1975. This annual event is held in honor of his mentor, Dr. Robert Stewart, Professor Emeritus and the first department chair in Computer Science at ISU. 

To learn more, go to: https://www.cs.iastate.edu/robert-stewart-distinguished-lecture