Random Number Generation: A Practitioner&#039;s Overview

	Special Guest Lectures
	Random Number Generation: A Practitioner's Overview
	Prof. Michael Mascagni, Department of Computer Science and School of Computational Science, Florida State University

	Johnston Hall 338 November 29, 2006 - 02:30 pm
Abstract: We will look at random number generation from the point-of-view of Monte Carlo computations. Thus, we will examine several serial methods of pseudorandom number generation and two different parallelization techniques. Among the techniques discussed with be "parameterization," which forms the basis for the Scalable Parallel Random Number Generators (SPRNG) library. SPRNG was developed several years ago by the author, and has become widely used within the international Monte Carlo community. SPRNG is briefly described, and the lecture ends with a short revue of quasirandom number generation. Quasirandom numbers offer many Monte Carlo applications the advantage of superior convergence rates. More information about SPRNG can be viewed at: http://sprng.fsu.edu/

Speaker's Bio: Dr. Mascagni obtained a B.S.E. in Biomedical Engineering, and a B.S. in Mathematics from the University of Iowa, and was a member of Phi Beta Kappa and Tau Beta Pi, the top liberal arts and engineering academic honor societies. Following, he obtained his M.S. and Ph.D. in Mathematics from the Courant Institute of Mathematical Sciences. Upon graduation, he obtained a post-doctoral research position in the Mathematical Research Branch of an institute of the National Institutes of Health, in Bethesda, MD and then moved to Washington, DC. It was during this period his research moved away from modeling the nervous system to studying the high-performance computing implications of the algorithms he developed and used. He was one of the first to use random number-based algorithms on the massively parallel Connection Machine at the Naval Research Lab in DC. In fact, after two years at NIH he moved to the Institute for Defense Analyses' Supercomputing Research Center in Bowie, MD. This organization works for the National Security Agency, and it was here that his interests in parallel computing, random number generation, number theory, and discrete mathematics were nurtured. Afterwards, Dr. Mascagni decided to rejoin academia, and went to the University of Southern Mississippi to run the Graduate Program in Scientific Computing. After a few years there, a desire to join a Computer Science department arose, and he moved to Florida State University as an Associate Professor of Computer Science, he has since been promoted to Full Professor. At present, his research group's focus is on parallel and distributed computing, Grid computing, random number generation, Monte Carlo methods, computational number theory and discrete algorithms, and applications to materials science, biochemistry, electrostatics, and finance. For a full biography, please visit: http://www.cs.fsu.edu/~mascagni/biography.html

Refreshments will be served.
This lecture has a reception.

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