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C2 People

C2 Members have a variety of backgrounds and experiences.  

Rather than contacting individual members directly, please email the group in all correspondence: c2questions@lists.stanford.edu

 

Remmelt Ammerlaan

Interests: data-science and its applications to large scale projects such as construction, infrastructure and city management

Steven Brill

Interests: Computational fluid dynamics, higher order methods for numerical PDEs, and high performance computing.

Anjan Dwaraknath

Interests: Numerical Linear Algebra, Optimization, Partial Differential Equations

Ron Estrin

Interests: Numerical Linear Algebra, Optimization

Brad Nelson

Interests: Numerical Linear Algebra, Structured Matrices, Partial Differential Equations, Fast Algorithms for Scientific Computing

Nolan Skochdopole

Interests: Discrete Math, Complexity Theory, Graph Theory, Algorithms

Faculty: Margot Gerritsen

Margot Gerritsen's main interest is the design and analysis of efficient numerical solution methods for partial differential equations that arise in fluid dynamics. Her PhD thesis work emphasized mathematical techniques. Since, her focus has shifted to using such techniques for actual engineering applications.

Faculty: Michael Saunders

Saunders develops mathematical methods for solving large-scale constrained optimization problems and large systems of equations. He also implements such methods as general-purpose software to allow their use in many areas of engineering, science, and business. He is co-developer of the large-scale optimizers MINOS, SNOPT, SQOPT, PDCO and the linear equation solvers SYMMLQ, MINRES, MINRES-QLP, LSQR, LSMR, and LUSOL. Stanford Engineering profile

Faculty: Reza Zadeh

Reza Zadeh focuses on discrete applied mathematics, machine learning theory and applications, and large-scale distributed computing. He has built large-scale distributed algorithms for the singular value decomposition on Spark, built the machine learning behind Twitter's who-to-follow system, and created other large-scale distributed machine learning systems.