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Celebrating Ravi Kannan and his 60th Birthday

Ravi Kannan
BY Byron Spice - Wed, 2013-05-22 11:05  Printer-friendly version

A few of Ravi Kannan's friends and colleagues — including some of the biggest names in theoretical computer science, optimization and discrete math — will gather May 23-25 at Carnegie Mellon University for a workshop celebrating his 60th birthday.

A former computer science faculty member at CMU, Kannan is principal researcher in the Algorithms Research Group at Microsoft Research India in Bangalore. At CMU, Kannan was a founder of the Algorithms, Combinatorics, and Optimization Program, an interdisciplinary Ph.D. program offered by the Computer Science Department, Tepper School of Business and the Department of Mathematical Sciences. Recently, he has returned as an adjunct faculty member. He also previously taught at MIT and Yale University.

Speakers at the RK60 workshop include Dick Karp of UC Berkeley, Lazlo Lovasz of Eëtvës Loránd University in Hungary, John Hopcroft of Cornell University, Persi Diaconis of Stanford University and other notables.

Much of Kannan's work has focused on efficient algorithms for mathematical and geometric problems in computer science. One of his research papers, published in 1990, has been called by some one of the most remarkable algorithmic achievements ever. The paper, written with Martin Dyer of the University of Leeds and Alan Frieze, professor of mathematical sciences and computer science at Carnegie Mellon, shows how to efficiently produce estimates of the volumes of arbitrary high-dimensional convex sets. The method they developed uses random walks to do geometric sampling, a technique now central to the theory of algorithms.

Kannan was recognized for that work with the 1991 Fulkerson Prize for Discrete Mathematics. He also was the 2011 winner of the Association for Computing Machinery's Knuth Prize in recognition of his influential work spanning many areas of theoretical computer science including lattices and their applications, geometric algorithms, machine learning, and computational linear algebra.

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