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Andrew
Rau-Chaplin
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   • Parallel Computing
   • Risk Analytics
 
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Welcome. I'm a professor in the Faculty of Computer Science at Dalhousie University where I teach courses in parallel computing, algorithms, and data structures.  My graduate students, collaborators, and I, pursue research projects that explore the application of parallel computing to data and computationally intensive problems. Our work is grounded in an algorithmic perspective, but we are committed to addressing the systems issues inherent in building working parallel applications, and performing systematic experimental evaluations.

Funding available for Postdocs and Graduate Students.

NSERC USRA Topics for Undergraduate Students

Catastrophe Modeling and Reinsurance Analytics. Catastrophe models and risk management systems play a critical role in the quantification and managment of the risk associated with natural catastrophes such as earthquakes, hurricanes, and floods. In this project we explore the design, implementation, and evaluation of Catastrophe Risk Modeling systems and Reinsurance Analytics that exploit advances in High Performance Computing (HPC).

Parallel Data Mining and OLAP. As the information age explodes with data, corporate and scientific data bases swell to previously unimagined sizes. This project investigates parallel methods to aid in data analysis and exploration.
Computational Bioinformatics. DNA and Protein sequence analysis problems are often both computationally and data intensive.  This project focuses on working with Biologists and Biochemists on the design and implementation of parallel tools for fundamental problems in DNA and Protein sequence analysis and phylogeny.
Parallel Geometric Algorithms. Geometric problems abound in applications from computational biology to geographic information systems. This project focuses on the design and implementation of parallel CGM algorithms for fundamental geometric/spatial operations and data structures.

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