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New approach to designing Monte Carlo algorithms

Research Achievements

New approach to designing Monte Carlo algorithms

Vakili supervised the PhD research of two IGERT trainees: Tarik Borogovac and Gang Zhao. The research involved the development of a new computational approach to designing efficient Monte Carlo algorithms. New generic variance reduction techniques have been developed and their statistical properties analyzed. The method has been applied to sample problems in computational finance, computational physics, computational biology, and medical imaging with significant efficiency gains. Application of the method to large scale computations in financial portfolio risk estimation and radiation planning for cancer treatment is being considered. A productive and ongoing collaboration with Dr. Frank Alexander and colleagues at LANL was established through Borogovac’s internship at LANL. Results of the research were presented at three Winter Simulation conferences and at an NSF supported Monte Carlo workshop at Harvard University. Six papers have resulted from the work of these trainees.