Dr. Michael Mascagni received training and was awarded undergraduate degrees in Biomedical Engineering and Mathematics at the University of Iowa. He then went to New York City, where he began studying Theoretical Neuroscience at the Rockefeller University, but ended studying Mathematics at the Courant Institute of Mathematical Sciences at New York University where he was awarded a Masters and Ph.D. After a postdoc at the National Institutes of Health, he worked many years on Supercomputing at the Institute for Defense Analyses in DC before returning to academia. He joined the Computer Science Faculty at FSU in the 1999-2000 where he holds joint appointments in Mathematics and Chemical Engineering, and is an active Faculty Associate in the School of Computational Science (CSC).
My research group focuses on all aspects of stochastic computing. We are interested in the development of Monte Carlo and Quasi-Monte Carlo algorithms; the applications of these algorithms in materials science, biochemistry, and physics; and the creation of computational infrastructure to support effective stochastic computing. We view Monte Carlo algorithms as any algorithms that use random number to compute quantities of interest. As such, we have developed extremely efficient Monte Carlo for problems ranging from the computation of permeabilities of packed beds to the electrostatic field elicited on large biomolecules in ionic solution. In addition, we study basic computational kernels, such as those involved in numerical linear algebra, to find effective Monte Carlo alternatives to existing, deterministic, algorithms. Finally, we have been involved in the development of computational infrastructure for stochastic computing. We developed the Scalable Parallel Random Number Generators (SPRNG) library that is very widely used in highly demanding Monte Carlo applications. SPRNG was an outgrowth of our own research in pseudo- and quasirandom number generation which continues in concert with SPRNG development. Also, building on SPRNG, we have developed extensive infrastructure for stochastic computing on the computational grid. In all these activities we have had undergraduate and graduate students, as well as postdoctoral scholars, visitors, and international collaborators working with me in our research group.
- M. Mascagni and N. A. Simonov (2004), "The Random Walk on the Boundary Method for Calculating Capacitance," Journal of Computational Physics, 195(2): 465-473.
- N. A. Simonov and M. Mascagni (2004), "Random Walk Algorithms for Estimating Electrostatic Properties of Large Molecules," Proceedings of The International Conference on Computational Mathematics (ICCM-2004), Novosibirsk, Russia, G. A. Mikhailov, V. P. Il'in, and Y. M. Laevsky, eds., ICM&G Publisher, Novosibirsk, Russia, Part I: 352-358.
- Y. Li and M. Mascagni (2003), "Analysis of Large-scale Grid-based Monte Carlo Applications," International Journal of High Performance Computing Applications (IJHPCA), 17(4): 369-382.
- M. Mascagni and A. Srinivasan (2000), "Algorithm 806: SPRNG: A Scalable Library for Pseudorandom Number Generation," ACM Transactions on Mathematical Software, 26: 436-461.