October 31, 2014 at 11am at the Dodd Research Center Auditorium (Storrs Campus)
This talk describes three different kinds of data that algorithm designers use to test their implementations. Selecting input data for past problems typically involves scholarship to assemble existing data and ingenuity to model it efficiently. Selecting data for present problems should be based on active discussions with users and careful study of existing data. Selecting data to model problems that may arise in the future is the most interesting and delicate of the tasks that we will consider. (This talk supplements an invited paper in the 2014 Symposium on Experimental Algorithms; it expands some of the stories in that paper, and tells one new story.)
Jon Bentley’s research interests include programming techniques, algorithm design, and the design of software tools and interfaces. He has written over a hundred articles on a variety of topics, ranging from the theory of algorithms to software engineering. His books include Writing Efficient Programs (1982) and Programming Pearls (2nd Edition 2000). He received a B.S. from Stanford in 1974 and an M.S. and Ph.D. from the University of North Carolina in 1976, then taught Computer Science at Carnegie Mellon for six years. He joined Bell Labs Research in 1982, and retired in 2001 to join Avaya Labs Research, from which he retired in 2013. He has been a visiting faculty member at West Point and Princeton, and has been a member of teams that have shipped software tools, telephone switches, telephones and web services. He received the Dr. Dobb’s Excellence in Programming Award in 2000.