Dr. Rau-Chaplin received a M.C.S. and
Ph.D. from Carleton University in Ottawa in 1990 and 1993, respectively.
From 1993 to 1994 he was a Postdoctoral Fellow at DIMACS - a National
Science Foundation center run by Princeton University, Rutgers, and AT&T
Bell labs. In 1994 he joined the Technical University of Nova Scotia,
in 1998 be became an Associate Professor in the Faculty of Computer
Science at Dalhousie University where he currently is currently a Professor.
He is interested in the application of parallel computing to a wide range of problem domains including data mining, image processing, solid modeling, geographic information systems (GIS), computational geometry/CAD, and AI. His parallel computing research program tends to be almost evenly divided between theoretical studies, where the focus is on developing asymptotically optimal or near optimal algorithms in a given complexity model, and implementation work which is used to guide and "ground" the theoretical work.
Much of his recent research has concentrated on coarse grained and BSP style parallel processing. Together with Frank Dehne and Andreas Fabri he introduced the CGM model which aims at delivering practical parallel algorithms whose performance analysis matches very closely the actually observed running times on commercial parallel machines. This model has been very successful in practice. Since 1993, an increasing number of researchers and graduate students have used his CGM model for parallel algorithm design in various application areas, and Algorithmica dedicated a special issue to the CGM model.
Recently, Dr Rau-Chaplin's research has focussed on research questions at the intersection of analytics, risk management, and high performance computing. Our research projects address challenges in catastrophe modeling, portfolio risk management, and dynamic financial analysis, by drawing on a diverse set of technologies including stochastic simulation, high performance computing, optimization, spatial OLAP, and data warehousing. For more information please see www.risk-analytics-lab.ca
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