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  • Thursday, October 13, 2016
  • 3:40 PM–4:30 PM
  • Science Building 110

Michael T. Goodrich, University of California, Irvine

We formalize a problem we call combinatorial pair testing (CPT), which has applications to the identification of uncooperative or unproductive participants in pair programming, massively distributed computing, and crowdsourcing environments. We give efficient adaptive and nonadaptive CPT algorithms and we show that our methods use an optimal number of testing rounds to within constant factors. We also provide an empirical evaluation of some of our methods. Joint work with David Eppstein and Daniel Hirschberg. See:
http://arxiv.org/abs/1305.0110

Prof. Goodrich received his B.A. in Mathematics and Computer Science from Calvin College in 1983 and his PhD in Computer Sciences from Purdue University in 1987.  He is a Chancellor's Professor at the University of California, Irvine, where he has been a faculty member in the Department of Computer Science since 2001.  Dr. Goodrich's research is directed at the design of high performance algorithms and data structures with applications to information assurance and security, the Internet, machine learning, and geometric computing.  With over 300 publications, including several widely-adopted books, his recent work includes contributions to efficient and secure distributed data structures, information privacy, social networks, and cloud security.  He is an ACM Distinguished Scientist, a Fellow of the American Association for the Advancement of Science (AAAS), a Fulbright Scholar, a Fellow of the IEEE, and a Fellow of the ACM. He is a recipient of the IEEE Computer Society Technical Achievement Award, the Brown Univ. Award for Technological Innovation, and the Pond Award for Excellence in Undergraduate Teaching.

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