Monte Carlo methods use statistical sampling to solve problems in fields of study as diverse as structural biology and finance.  In fact, there are many problems that are computationally intractable using deterministic methods, and so Monte Carlo is the only feasible method of solution.  In addition, Monte Carlo is a naturally parallel computational paradigm, and so it has scalability properties that most deterministic methods envy.  In this talk we consider how Monte Carlo methods are suitable for solving a wide variety of problems normally solved with deterministic methods, and the implications of this for extreme high-performance computing.  Students that know Monte Carlo, high-performance computing, and the probability and statistics that supports Monte Carlo have been and continue to be in great demand in the financial services industry.

Bio:

Michael Mascagni is an internationally recognized expert on all aspects of random number generation and Monte Carlo methods, and has lectured extensively across the globe on theses and related topics.  He received his undergraduate degrees in Biomedical Engineering and Mathematics at the University of Iowa in 1981, and entered Rockefeller University to study neurobiology.  While taking some math courses at NYU he decided to switch to math, and he moved to the Courant Institute in 1983.  He graduated in 1987, having worked with Prof. Charlie Peskin on the numerical solution of nerve equations.  He has published over 100 scholarly articles, has graduated doctoral students in Computer Science, Mathematics, and Scientific Computing, and he currently leads a research group working in high-performance computing aspects of Monte Carlo methods and random number generation.  He is an editor for many journals including Monte Carlo Methods and Applications, Mathematics and Computers in Simulation, and the ACM Transactions on Mathematical Software.  He has been a visiting faculty member at Université de Toulon et du Var, Universität Salzburg, Universität Kaiserslautern, Università degli Studi di Padova, and the King Abdullah University of Science and Technology.  He also spent a sabbatical year visiting the Seminar für Angewandte Mathematik, Departement Mathematik, Eidgenössische Technische Hochschule (ETH-Zürich).  He was elected an Association for Computing Machinery (ACM) Distinguished Scientist in 2011, and is currently a Faculty Appointee at the National Institute of Standards and Technology (NIST).

http://www.cs.fsu.edu/~mascagni/