John Kieffer

Professor

Research Area: Information theory

kieffer@umn.edu

Area of Expertise:

Source coding, data compression

Faculty Affiliations:

University of Missouri Rolla, Dept of Math and Stat, 1970-1986
University of Minnesota Twin Cities, Dept of ECE, 1986-2011

Education:

Phd, Mathematics, 1970, University of Illinois Urbana
MS, Mathematics, 1968, University of Illinois Urbana
BS, Applied Math, 1967, University of Missouri Rolla

Honors/Awards:

Fellow IEEE 1993
Life Fellow IEEE 2017

Synopsis:

Journal Publications

Google Scholar Page

Publications:

John Kieffer, A Survey of Bratteli Information Source Theory
Proceedings of the 2016 International Symposium on Information Theory, pp. 16-20, 2016.

Jie Zhang, En-hui Yang, John Kieffer, A universal grammar-based code for lossless compression of binary trees
IEEE Transactions on Information Theory, Vol. 60, pp. 1373-1386, 2014.

Jie Zhang, En-hui Yang, John Kieffer, Redundancy analysis in lossless compression of a binary tree via its minimal DAG representation
Proceedings of the 2013 International Symposium on Information Theory, pp. 1914-1918, 2013.

John Kieffer, Asymptotics of divide-and-conquer recurrences via iterated function systems
Proceedings of 23rd Intern. Meeting on Probabilistic, Combinatorial, and Asymptotic Methods for the Analysis of Algorithms (AofA’12), pp.55-66, 2012.

John Kieffer, A catalog of self-affine hierarchical entropy functions
Algorithms (Basel), Vol. 4, pp. 307-333, 2011.