FASTMath SciDAC Institute
Research Team

The FASTMath team brings together preeminent scientists in a broad range of applied mathematics areas. The FASTMath team has a proven track record of developing new mathematical technologies and algorithms, tackling difficult algorithmic and implementation issues as computer architectures undergo a fundamental shift, and engaging multiple application domains to enable new scientific discovery. 

More than 50 mathematicians from five national laboratories and five universities comprise our team.

Oak Ridge National Laboratory

We are focused on the development of all aspects of exascale data analytic methods. Efforts include the functional representation of data, using sparse polynomial approximation, low dimensional manifolds, and high order regularizers to enable faster storage, retrieval, and analysis of large datasets. Targeted methods are being developed in sparse storage and retrieval of large data, uncertainty estimates for sparse data representation, fast estimation of data statistics, and importance ranking in streaming data.


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Ben Whitney
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Hoang Tran
Rick Archibald

Rensselaer Polytechnic Institute

We are focused on the development of all aspects of exascale unstructured mesh methods. Efforts include the development of parallel mesh infrastructures, general anisotropic mesh adaptation procedures for general 3D domains including curved high order mesh entities, unstructured mesh adjacency based partitioning tools, interactions with general geometry representations, unstructured mesh particle methods, unstructured mesh adaption in stochastic spaces for UQ, and an unstructured mesh fields infrastructure.


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Seegyoung Seol
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Cameron Smith
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Onkar Sahni
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George Slota
Mark Shephard

Rensselaer Polytechnic Institute, Scientific Computation Research Center, 110 Eighth Street, 4019 CII Building, Troy, NY 12180-3590


Sandia National Laboratories

We contribute to technical areas across FASTMath. We are the lead institution for Uncertainty Quantification research in FASTMath, performing new research and working with a number of SciDAC applications. Our development in KokkosKernels addresses performance portability across multicore and GPU platforms, and our architecture-aware partitioning and mapping help applications run efficiently on emerging architectures.  The Trilinos solver packages MueLu and ShyLu provide multigrid and hybrid direct/iterative solvers and preconditioners to applications.  Our Albany framework, integrated with RPI's adaptive mesh capabilities, continues to provide component-based capabilities for rapid application development. New efforts include optimization and physics-based preconditioning, supporting collaborations in ice-sheet and materials modeling.


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Siva Rajamanickam
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Gianluca Geraci
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Mauro Perego's picture
Jonathan Hu
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Mike Eldred
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Habib Najm
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Andy Salinger
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Mehmet Deveci
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John Jakeman
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Khachik Sargsyan
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Bert Debusschere
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Daniel Ibanez
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Cosmin Safta
Karen Devine

Sandia National Laboratories, P.O. Box 5800, MS 1318, Albuquerque, NM 87185-1318


Southern Methodist University

Our FASTMath research and development focuses on time integration methods and nonlinear solvers for multi-rate problems arising from multi-physics and multi-scale simulations.  Specifically, we are exploring combination implicit–explicit methods, symplectic methods, and integrators for coupled continuum–particle models.


Daniel Reynolds

Southern Methodist University Mathematics, P.O. Box 750156, Dallas, TX 75275-0156