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Scientists and engineers rely more than ever on computer modeling and simulation to guide their experimental and design work. The infrastructure that supports this activity depends critically on the development of new numerical algorithms that are reliable, efficient, and scalable. "Large N" is the hallmark of modern, data-intensive scientific computing and it is a common thread that unifies departmental research in numerical linear algebra, optimization, and partial differential equations.
Faculty and Researchers
David Bindel works on numerical linear algebra, numerical methods for data science, and simulating microelectromechanical systems and fusion plasmas. His research involves software design, mathematical analysis and physical modeling.
Anil Damle works on the development of fast algorithms in applied and computational mathematics that exploit structure coming from underlying physical or statistical models. This includes work in the areas of computational quantum chemistry, numerical linear algebra, and spectral clustering.
Giulia Guidi works in the field of high-performance computing for large-scale computational sciences (in particular, computational biology). Her research involves the development of algorithms and software infrastructures on parallel machines to accelerate data processing without sacrificing programming productivity and to make high-performance computing more accessible.
The scientific computing group is also active in the Applied Mathematics Ph.D. program, which is part of Cornell's Center for Applied Mathematics. Prospective Ph.D. applicants interested in the mathematical aspects of scientific computing may wish to consider that graduate field as well.