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Biography. Guy L. Steele Jr. is a Sun Fellow at Sun Microsystems, Inc. He received his A.B. in applied mathematics from Harvard College (1975), and his S.M. and Ph.D. in computer science and artificial intelligence from M.I.T. (1977 and 1980). He has also been an assistant professor of computer science at Carnegie Mellon University; a member of ...

Guy L. Steele Jr. believes that it should not be the programmer’s job to think about parallelism, but languages should provide ways to transparently run tasks in parallel. This requires a new approach in building languages supporting algorithms built on independence and build-and-conquer principles rather than on linear decomposition of problems.

Media in category "Guy L. Steele Jr." The following 8 files are in this category, out of 8 total.

Manzil Zaheer, Jean-Baptiste Tristan, Michael L. Wick, Guy L. Steele Jr. 02 Sep 2023 (modified: 21 Jul 2022) Submitted to ICLR 2017 Readers: Everyone Abstract: Static analyzers are meta-programs that analyze programs to detect potential errors or collect information.

David M. R. Park, Daniel P. Friedman, David S. Wise, Guy L. Steele Jr.: Proceedings of the 1982 ACM Symposium on LISP and Functional Programming, LFP 1980, Pittsburgh, PA, USA, August 15-18, 1982. ACM 1982 , ISBN 0-89791-082-6

As a dialect of the Lisp language, Scheme was created by Gerald J. Sussman and Guy L. Steele Jr. at MIT around 1975. In 1991, statisticians Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, embarked on an S implementation.

PACMPL (ICFP) seeks contributions on the design, implementations, principles, and uses of functional programming, covering the entire spectrum of work, from practice to theory, including its peripheries. Authors of papers published in this issue of PACMPL will present their work at during the in-person conference, providing an opportunity for researchers and developers to hear about the latest ...

Jean-Baptiste Tristan, Joseph Tassarotti, Guy L. Steele Jr.: Efficient Training of LDA on a GPU by Mean-for-Mode Estimation. ICML 2015: 59-68