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Georges Grinstein


Georges Grinstein

Center for Biomolecular and Medical Informatics, USA

Biography

Georges Grinstein is Professor of Computer Science at the University of Massachusetts Lowell, was head of its Bioinformatics Program, Director of its Institute for Visualization and Perception Research, and now Chief Scientific Officer of Weave Visual Analytics. He received his Ph.D. in Mathematics from the University of Rochester in 1978. His work is broad and interdisciplinary, covering the perceptual and cognitive foundations of visualization, very high-dimensional data visualization, visual analytics and applications. The emphasis is on the modeling, visualization, and analysis of complex information data and systems. He has over 40 years in academia with extensive consulting, over 300 research grants, products in use nationally and internationally, several patents, numerous publications in journals and conferences, a book on interactive data visualization, founded several companies, been the organizer or chair of national and international conferences and workshops in Computer Graphics, in Visualization, and in Data Mining. He has given numerous keynotes and mentored over 40 doctoral students and hundreds of graduate students. He has been on the editorial boards of several journals in Computer Graphics and Data Mining, a member of ANSI and ISO, a NATO Expert, and a technology consultant for various government agencies and commercial organizations. For the last ten years he has co-chaired the IEEE VAST Challenges in visual analytics leading to new research areas. He has developed and taught new courses, one of which Radical Design focused on how to develop radical new products instead of evolutionary ones. He is a member of the Department of Homeland Security’s Center of Excellence CCICADA (Command, Control and Interoperability Center for Advanced Data Analysis), and directs the development of Weave, an open source web-based interactive collaborative visual analytics system incorporating numerous innovations.

Abstract

Abstract : Anticipatory linked context sensitive access to broadly heterogeneous data