Computational Geometry: Paradigms for Bridging Disciplines
Computational geometry deals with the design, analysis, and applications of algorithms and tools for solving geometric problems. In this talk the ability of computational geometry to provide paradigms for bridging disciplines will be illustrated by means of three examples: (1) the application of proximity graphs to computer vision, machine learning, wireless communication, and statistics, (2) reconfiguration of geometric structures and its application to robotics, bioinformatics, and music technology, and (3) maximally even sets and their application to neutron source accelerators, astronomical calendar design, computer graphics, number theory, music theory and pattern analysis and design. In addition, directions for future research in these areas will also be outlined.
Godfried Toussaint received a PhD in electrical engineering in 1972 from the University of British Columbia in Vancouver, Canada. Since then he has been teaching and doing research in the School of Computer Science at McGill University in Montreal, Canada, in the areas of information theory, pattern recognition, computational geometry, instance-based learning, music information retrieval, and computational music theory. On September 1, 2007 he was promoted to Professor Emeritus. In 2005 he also became a researcher in the Centre for Interdisciplinary Research in Music Media and Technology, in the Schulich School of Music at McGill University. He is a founder of several conferences and workshops, an editor of several journals, has received numerous awards, and has published more than 350 papers. In 2009 he was awarded a Radcliffe Fellowship by the Radcliffe Institute for Advanced Study at Harvard University for the 2009-1010 academic year.