Team

Carol Woodward, LLNL

Todd Munson, ANL

Esmond G. Ng, LBNL

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.

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.

Mark Shephard (RPI) focsues on technologies including  automatic mesh generation of CAD geometry, automated and adaptive analysis methods, and parallel adaptive simulation technologies. 

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)

Ken Jansen (CU Boulder)

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.

George Slota (RPI)

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.

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.

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.

Chris Vogl (LLNL) is a research scientist working on the development, analysis, and implementation of numerical methods for PDEs, with a particular focus on multiscale, multiphysics systems. 

Ulrike Meier Yang (LLNL) is a computational mathematician with expertise in parallel numerical methods, including multigrid methods, high performance computing, and scientific software design. 

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. 

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. 

Christian Glusa (SNL) works on scalable solvers (multigrid, domain decomposition) and hierarchical matrix approximations, with applications to electromagnetics and nonlocal equations. 

Jonathan Hu (SNL)

Ozan Karsavuran (LBNL) is a computer scientist working on both sparse symmetric positive definite and indefinite matrix factorization algorithms, emphasizing high-performance computing to improve runtime and scalability. 

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. 

Peter McCorquodale (LBNL)

Esmond G. Ng (LBNL) works on numerical linear algebra, with a focus on sparse matrices. His research includes computational complexity, mathematical software development, and parallel computing. 

Wayne Mitchell (LLNL) is an applied mathematician and developer of hypre with expertise in multigrid methods, linear solvers, and high-performance computing. 

Siva Rajamanickam (SNL)

Ichitaro Yamakazi (SNL)

Chao Yang (LBNL)

Esmond G. Ng (LBNL) works on numerical linear algebra, with a focus on sparse matrices. His research includes computational complexity, mathematical software development, and parallel computing. 

Jeffrey Larson (ANL) develops specialized methods for the numerical optimization of expensive-to-evaluate systems or simulations. 

Toby Isaac (ANL)

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. 

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. 

Juliane Mueller (NREL) focuses on derivative-free optimization algorithms and has expertise in developing surrogate models, active learning and experimental design strategies, and machine learning. 

Todd Munson (ANL)

Evan Toler (ANL)

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. 

Tiernan Casey (SNL)

Bert Debusschere 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.  (SNL)

Michael Eldred (SNL)

Roger Ghanem (USC)

Khachik Sargsyan (SNL) develops and deploys algorithms for uncertainty quantification and statistical learning in the context of large scale physical and computational models. 

Rick Archibald (ORNL) works on foundational AI/ML for scientific application and HPC.

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 (NREL) is a computational statistician focusing on statistical and machine learning modeling, uncertainty quantification and data science.

Viktor Reshniak (ORNL) is a computational mathematician specializing in machine learning algorithms, data compression, and scientific data analysis.