Related Areas

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

Austin Benson develops computational frameworks for analyzing large-scale and complex datasets coming from the Web, social networks, biology, and other scientific domains. This involves a combination of network science, matrix and tensor computations, data mining, machine learning, algorithm design, and high-performance computing.

David Bindel works on simulating microelectromechanical systems, numerical linear algebra, finite element analysis, floating point computation and network tomography. 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.

Madeleine Udell studies optimization and machine learning for large scale data analysis and control, with applications in marketing, demographic modeling, medical informatics, and engineering system design. She also develops libraries for modeling and solving optimization problems, including Convex.jl, one of the top ten tools in the Julia language for technical computing.

Charlie Van Loan works in numerical linear and multilinear algebra. A recurring theme in his current research is the development of efficient techniques for matrix problems that involve Kronecker products and multiple symmeties. He has written several texts including Matrix Computations (with Gene Golub), Introduction to Scientific Computing, and Computational Frameworks for the Fast Fourier Transform.

Applied Mathematics

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.

Related Research

In addition to the core faculty mentioned above, we have several colleagues who work in related areas:

Kavita Bala performs research on scalable graphics for high-complexity scenes. Emphasis is on feature-based graphics, real-time global illumination, perceptually-based rendering, image-based rendering and texturing.

Steve Marschner focuses on high-quality rendering with an emphasis on accurate models for the appearance of everyday materials. Analytical and numerical calculations of light reflection and radiative transport are key elements of his research.

Ramin Zabih applies combinatorial and numerical algorithms to problems in computer vision and medical imaging.