David Brown is the team leader of the Numerical Analysis and Parallel Computing team in the Computing Information and Communications (CIC) division at Los Alamos National Laboratory, Los Alamos, New Mexico. His team, which is part of the Scientific Computing Group, is made up of thirteen members, twelve have Ph.D.'s in applied math, physics, computer science or engineering discipline. and one is an applied mathematician with a master's degree. Two of the team members are postdoctoral fellows; the rest are Laboratory staff members.
"The Scientific Computing Group," explains David, "is a group of mostly Ph.D. scientists who do research in the areas of numerical methods for fluid dynamics, combustion, and radiation transport, and who also study and evaluate advanced computer architectures. The charter of the Numerical Analysis and Parallel Computing team is to perform research in the area of numerical methods for partial differential equations (PDEs) found in physical models of relevance to the Laboratory. Research is focused in the areas of numerical algorithms, mathematical analysis, and serial and parallel computing. We are currently focused on the development of computational tools for the solution of partial differential equations in regions of complex geometry."
"The development of effective advanced computational tools for solving PDEs requires a thorough background in applied mathematics, numerical analysis and at least one scientific or engineering discipline. We are involved in the design of methods as well as the design of the computational tools, and the former requires significant mathematical background."
David has a B.S. in Physics and an M.S. in Geophysics from Stanford University, and a Ph.D. in Applied Mathematics from the California Institute of Technology. He has been at Los Alamos for 12 years, starting as a post doctoral fellow and joining the staff after two years. He became team leader for his team about one and a half years ago.
"When I first left graduate school, I was open to the possibility of working in academics, industry or at a National Laboratory," David recalls. "The National Lab environment turned out to be well suited to my preference for spending most of my time engaged in research. Also, at that time, the computer facilities available at the National Labs were second to none. In recent years, this has become less of an issue as the computational power of scientific workstations has increased. However, the national lab environment provides an opportunity to learn about and work on a wide variety of applications, and the access to very high performance computational facilities is still quite unique at the labs."
Someone interested.in working in David's area should pursue a background in the numerical solution of differential equations, with an emphasis both on numerical analysis and on scientific applications. However, it is also important to take advantage of summer internship programs in industry and at the government labs. That is one of the best ways to get a feel for the research environment and to understand the kinds of problems that the goverment and industry are interested in solving.
"It is important to have a solid background in basic applied mathematics and an in-depth understanding of one or more application areas as well," he adds. "In today's environment, it is also important to be familiar with, and preferably experienced in modern object-oriented techniques of software design and implementation, and the corresponding object-oriented languages, such as C++. It is also important to understand modern computer architectures, since algorithm design can often be architecture specific. Thus, appropriate computer science courses could be valuable."
"Working in a nonacademic research environment requires the capability to think independently and creatively, to be self-directed and flexible, and also to work well in a team environment."
David also has a home page on the LANL Web site.
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