The Team
The Team
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Director:
Carol Woodward, LLNL
Deputy Director:
Todd Munson, ANL
Adaptive and Multiscale Discretizations
Area Lead:
Mark Shephard (RPI) focsues on technologies including automatic mesh generation of CAD geometry, automated and adaptive analysis methods, and parallel adaptive simulation technologies.
Members:
Ann Almgren (LBNL) works on computational algorithms for solving PDEs and the development and implementation of new multiphysics adaptive mesh codes that are designed for the latest architectures.
Cody Balos (LLNL) is a computational scientist and research software engineer with expertise in time integration methods, efficient high-performance computing, and scientific software development. He is the RSE for SUNDIALS.
Erik Boman (SNL) is an expert in combinatorial scientific computing, in particular graph partitioning. He also works on HPC, sparse matrix computations, and numerical linear algebra.
Erik Boman (SNL) is an expert in combinatorial scientific computing, in particular graph partitioning. He also works on HPC, sparse matrix computations, and numerical linear algebra.
Veselin Dobrev (LLNL) is a computational mathematician working on finite element methods, preconditioning, adaptive meshing, and remap. He is a founding member and active developer of the MFEM library.
Veselin Dobrev (LLNL) is a computational mathematician working on finite element methods, preconditioning, adaptive meshing, and remap. He is a founding member and active developer of the MFEM library.
David Gardner (LLNL) is a computational scientist and SUNDIALS library developer with expertise in time integration and nonlinear solver methods for multiscale, multiphysics applications on HPC systems.
Hannah Klion (LBNL) works on block-structured adaptive mesh codes such as high-performance simulations of oceanic flows and plasma physics.
Hannah Klion (LBNL) works on block-structured adaptive mesh codes such as high-performance simulations of oceanic flows and plasma physics.
Tzanio Kolev (LLNL) works on finite elements; high-order methods for meshing, discretizations and solvers; and high-performance applications for solving PDEs on unstructured grids. He leads the MFEM project.
Tzanio Kolev (LLNL) works on finite elements; high-order methods for meshing, discretizations and solvers; and high-performance applications for solving PDEs on unstructured grids. He leads the MFEM project.
Jacob Merson (RPI)
Andrew Nonaka (LBNL) is interested in HPC implementations of multiphysics and multiscale algorithms for PDEs using structured adaptive mesh, particle/mesh and machine learning algorithms.
Jan Nikl (LLNL) is a computational mathematician and MFEM library developer. He is the RSE for MFEM
Socratis Petrides (LLNL) works on high-order Finite Element methods for the numerical solution of PDEs as well as multilevel solvers for various applications including solid mechanics and wave propagation problems.
Socratis Petrides (LLNL) works on high-order Finite Element methods for the numerical solution of PDEs as well as multilevel solvers for various applications including solid mechanics and wave propagation problems.
Daniel Reynolds (UMBC) is an applied mathematician with expertise in time integration methods for multi-physics models. He is a lead developer for the SUNDIALS library, and was the primary developer of its ARKODE solver.
Steven Roberts (LLNL) is an applied mathematician with expertise in time integration methods for systems with multiple time scales or model fidelities, and he is a developer for the SUNDIALS library.
Onkar Sahni (RPI) focuses on unstructured mesh-based high-fidelity and high-performance methods and codes, and data-driven techniques, for multi-physics/multi-scale transport problems.
Onkar Sahni (RPI) focuses on unstructured mesh-based high-fidelity and high-performance methods and codes, and data-driven techniques, for multi-physics/multi-scale transport problems.
Cameron Smith (RPI) works on simulation automation, load balancing, and unstructured meshing, on leadership-class systems using distributed and shared memory parallelism.
Cameron Smith (RPI) works on simulation automation, load balancing, and unstructured meshing, on leadership-class systems using distributed and shared memory parallelism.
Carol Woodward (LLNL) is interested in time integration and nonlinear iterative methods for solution of nonlinear PDES and in implementation and deployment of numerical software designed for high performance systems.
Algebraic Solvers
Area Lead:
Xiaoye Sherry Li (LBNL) works on high performance scientific computations, including numerical linear algeebra, sparse matrix algorithms, and scientific machine learning.
Xiaoye Sherry Li (LBNL) works on high performance scientific computations, including numerical linear algeebra, sparse matrix algorithms, and scientific machine learning.
Members:
Mark Adams (LBNL) works in HPC, with multigrid solvers kinetic discretizations, all deployed in the PETSc numerical library where he has been a contributor for 25+ years, and work with applications in FES, HEP and BER.
Mark Adams (LBNL) works in HPC, with multigrid solvers kinetic discretizations, all deployed in the PETSc numerical library where he has been a contributor for 25+ years, and work with applications in FES, HEP and BER.
Robert Falgout (LLNL) is a computational mathematician whose work is focused primarily on the development of multilevel methods. He leads the hypre and XBraid projects.
Robert Falgout (LLNL) is a computational mathematician whose work is focused primarily on the development of multilevel methods. He leads the hypre and XBraid projects.
Riu Peng Li (LLNL) is a computational mathematician working on sparse matrix computations, parallel computing, iterative methods for solving linear systems, and preconditioning techniques.
Riu Peng Li (LLNL) is a computational mathematician working on sparse matrix computations, parallel computing, iterative methods for solving linear systems, and preconditioning techniques.
Lin Lin (LBNL)
Lin Lin (LBNL)
Yang Liu (LBNL) focuses on matrix and tensor algorithms for dense and sparse linear systems, uncertainty quantification and autotuning, and scientific machine learning for solving PDEs.
Yang Liu (LBNL) focuses on matrix and tensor algorithms for dense and sparse linear systems, uncertainty quantification and autotuning, and scientific machine learning for solving PDEs.
Victor Magri (LLNL) is a computational mathematician and hypre library developer working on HPC, multigrid methods, preconditioning, and linear solvers. He is the RSE for hypre.
Richard Tran Mills (ANL) works at the intersection of HPC simulation, data-driven science, and several application domains. He is a core developer of PETSc and an originating author of PFLOTRAN.
Richard Tran Mills (ANL) works at the intersection of HPC simulation, data-driven science, and several application domains. He is a core developer of PETSc and an originating author of PFLOTRAN.
Roel Van Beeumen (LBNL) works on numerical linear algebra and high-performance algorithms for solving large-scale linear and nonlinear eigenvalue problems, as well as model order reduction techniques.
Roel Van Beeumen (LBNL) works on numerical linear algebra and high-performance algorithms for solving large-scale linear and nonlinear eigenvalue problems, as well as model order reduction techniques.
Chao Yang (LBNL) is an expert in numerical linear algebra and high-performance computing, especially efficient algorithms and fast implementations for solving large-scale eigenvalue problems.
Chao Yang (LBNL) is an expert in numerical linear algebra and high-performance computing, especially efficient algorithms and fast implementations for solving large-scale eigenvalue problems.
Decision Support Methods
Area Lead:
Bert Debusschere (SNL) develops methods and software for enabling predictive simulation in science and engineering, aiming to use all forms of information for more confidence in answering questions in complex systems.
Deputy Area Lead:
Jeffrey Larson (ANL) develops specialized methods for the numerical optimization of expensive-to-evaluate systems or simulations.
Jeffrey Larson (ANL) develops specialized methods for the numerical optimization of expensive-to-evaluate systems or simulations.
Members:
Ahmed Attia (ANL) develops scalable computational algorithms and software tools for large-scale inverse problems, uncertainty quantification, and optimal experimental design, with applications to energy problems.
Julie Bessac (NLR) is a computational statistician focusing on statistical and machine learning modeling, uncertainty quantification and data science.
Julie Bessac (NLR) is a computational statistician focusing on statistical and machine learning modeling, uncertainty quantification and data science.
Emil Constantinescu (ANL) works on the foundations of AI/ML for scientific computing and UQ for inverse problems.
Michael Eldred (SNL) initiated the Dakota software project and performs algorithm research in uncertainty quantification (focus on multifidelity methods), surrogate-based optimization, and design under uncertainty.
Michael Eldred (SNL) initiated the Dakota software project and performs algorithm research in uncertainty quantification (focus on multifidelity methods), surrogate-based optimization, and design under uncertainty.
Gianluca Geraci (SNL) develops algorithms for uncertainty quantification and scientific machine learning with a particular emphasis on multi-fidelity methodologies.
Roger Ghanem (USC) works on uncertainty quantification and scientific machine learning with a focus on multi-scale and multi-physics interactions and high-dimensional statistics.
Roger Ghanem (USC) works on uncertainty quantification and scientific machine learning with a focus on multi-scale and multi-physics interactions and high-dimensional statistics.
Sven Leyffer (ANL) develops efficient and reliable methods for solving large-scale nonlinear optimization problems, and applies optimization techniques to digital twins and optimal experimental design.
Sven Leyffer (ANL) develops efficient and reliable methods for solving large-scale nonlinear optimization problems, and applies optimization techniques to digital twins and optimal experimental design.
Youssef Marzouk (MIT) develops methodologies for uncertainty quantification, Bayesian computation, and machine learning, motivated by a broad range of engineering and science applications.
Youssef Marzouk (MIT) develops methodologies for uncertainty quantification, Bayesian computation, and machine learning, motivated by a broad range of engineering and science applications.
Matt Menickelly (ANL) develops algorithms for optimization of expensive-to-evaluate, possibly noisy/stochastic simulations/experiments. Matt also works in the application of AI/ML techniques to such algorithms.
Matt Menickelly (ANL) develops algorithms for optimization of expensive-to-evaluate, possibly noisy/stochastic simulations/experiments. Matt also works in the application of AI/ML techniques to such algorithms.
Todd Munson (ANL) works on numerical optimization methods for high-performance architectures and is experienced in nonlinear, constrained, and discrete problems and variational inequalities.
Todd Munson (ANL) works on numerical optimization methods for high-performance architectures and is experienced in nonlinear, constrained, and discrete problems and variational inequalities.
Habib Najm (SNL) works on the development of numerical methods, computational algorithms, and software for uncertainty quantification in large scale computational models of physical systems.
Habib Najm (SNL) works on the development of numerical methods, computational algorithms, and software for uncertainty quantification in large scale computational models of physical systems.
Mauro Perego (SNL) is a computational mathematician working on discretization and solution of nonlinear partial differential equations, numerical optimization, and scientific machine learning.
Mauro Perego (SNL) is a computational mathematician working on discretization and solution of nonlinear partial differential equations, numerical optimization, and scientific machine learning.
Cosmin Safta (SNL) develops uncertainty quantification and scientific machine learning algorithms focused on improving predictive capabilities of large scale computational models for science and engineering applications.
Cosmin Safta (SNL) develops uncertainty quantification and scientific machine learning algorithms focused on improving predictive capabilities of large scale computational models for science and engineering applications.
Khachik Sargsyan (SNL) develops and deploys algorithms for uncertainty quantification and statistical learning in the context of large scale physical and computational models.
Khachik Sargsyan (SNL) develops and deploys algorithms for uncertainty quantification and statistical learning in the context of large scale physical and computational models.
Hoang Tran (ORNL) is an applied mathematician specializing in high-dimensional approximation, scientific machine learning and data analysis.
Stefan Wild (LBNL) focuses on model-based algorithms & software for numerical optimization and automated learning; he tackles problems involving advanced computer simulations, complex data, and physical experiments.
Stefan Wild (LBNL) focuses on model-based algorithms & software for numerical optimization and automated learning; he tackles problems involving advanced computer simulations, complex data, and physical experiments.
Efficiency Crosscut
Area Lead:
Cody Balos (LLNL) is the Efficiency CrossCut Lead. He s a computational scientist and research software engineer with expertise in time integration methods, efficient high-performance computing, and scientific software development. He is the RSE for SUNDIALS.
Mark Adams (LBNL) works in HPC, with multigrid solvers kinetic discretizations, all deployed in the PETSc numerical library where he has been a contributor for 25+ years, and work with applications in FES, HEP and BER.
Mark Adams (LBNL) works in HPC, with multigrid solvers kinetic discretizations, all deployed in the PETSc numerical library where he has been a contributor for 25+ years, and work with applications in FES, HEP and BER.
Veselin Dobrev (LLNL) is a computational mathematician working on finite element methods, preconditioning, adaptive meshing, and remap. He is a founding member and active developer of the MFEM library.
Veselin Dobrev (LLNL) is a computational mathematician working on finite element methods, preconditioning, adaptive meshing, and remap. He is a founding member and active developer of the MFEM library.
Robert Falgout (LLNL) is a computational mathematician whose work is focused primarily on the development of multilevel methods. He leads the hypre and XBraid projects.
Robert Falgout (LLNL) is a computational mathematician whose work is focused primarily on the development of multilevel methods. He leads the hypre and XBraid projects.
David Gardner (LLNL) is a computational scientist and SUNDIALS library developer with expertise in time integration and nonlinear solver methods for multiscale, multiphysics applications on HPC systems.
Riu Peng Li (LLNL) is a computational mathematician working on sparse matrix computations, parallel computing, iterative methods for solving linear systems, and preconditioning techniques.
Riu Peng Li (LLNL) is a computational mathematician working on sparse matrix computations, parallel computing, iterative methods for solving linear systems, and preconditioning techniques.
Xiaoye Sherry Li (LBNL) works on high performance scientific computations, including numerical linear algeebra, sparse matrix algorithms, and scientific machine learning.
Xiaoye Sherry Li (LBNL) works on high performance scientific computations, including numerical linear algeebra, sparse matrix algorithms, and scientific machine learning.
Yang Liu (LBNL) focuses on matrix and tensor algorithms for dense and sparse linear systems, uncertainty quantification and autotuning, and scientific machine learning for solving PDEs.
Yang Liu (LBNL) focuses on matrix and tensor algorithms for dense and sparse linear systems, uncertainty quantification and autotuning, and scientific machine learning for solving PDEs.
Richard Tran Mills (ANL) works at the intersection of HPC simulation, data-driven science, and several application domains. He is a core developer of PETSc and an originating author of PFLOTRAN.
Richard Tran Mills (ANL) works at the intersection of HPC simulation, data-driven science, and several application domains. He is a core developer of PETSc and an originating author of PFLOTRAN.
Andrew Nonaka (LBNL) is interested in HPC implementations of multiphysics and multiscale algorithms for PDEs using structured adaptive mesh, particle/mesh and machine learning algorithms.
Siva Rajamanickam (SNL) is a computational scientist working on high performance computing, specifically in machine learning for science, performance portable algorithms, codesign of algorithms and architectures and combinatorial scientific computing. He is the RSE for Kokkos Kernels.
Cameron Smith (RPI) works on simulation automation, load balancing, and unstructured meshing, on leadership-class systems using distributed and shared memory parallelism.
Cameron Smith (RPI) works on simulation automation, load balancing, and unstructured meshing, on leadership-class systems using distributed and shared memory parallelism.
Research Software Engineers (RSEs)
Software Coordinator:
Ann Almgren (LBNL) is the FASTMath Software Coordinator and lead of the Software Cross-cutting activities.
Stephen Hudson (ANL) is one of two RSEs for libEnsemble.
Yang Liu (LBNL) is the RSE for SuperLU and STRUMPACK.
Victor Magri (LLNL) is the RSE for hypre.
John-Luke Navarro (ANL) is one of two RSEs for libEnsemble.
Jan Nikl (LLNL) is the RSE for hypre.
Doru Thom Popovici (LBNL) is the RSE for FASTEig.
Siva Rajamanickam (SNL) is the RSE for Kokkos Kernels.
Greg Von Winckel (SNL) is the RSE for Dakota and PyTUQ.
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