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.

Hans Johansen (LBNL) develops higher-order methods for a wide variety of PDEs, and specializes in finite volume embedded boundary approaches to complex geometries, implemented with fast solvers on GPUs.

Daniel Martin (LBNL) develops algorithms and software for solving systems of PDEs in a wide range of applications, with a particular focus on adaptive mesh refinement (AMR) and high (4th) order finite-volume methods.

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.

Nate Overton-Katz (LBNL) focuses on the development of cut-cell methods, adaptive mesh refinement, and high-performance computing for PDE modeling.

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.

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.

Dan Ibanez (SNL) is working on a collaborative effort to integrate PUMI with the Albany parallel multiphysics code.

Ken Jansen (U. CO Boulder) focuses on extreme scale simulation combined with in situ machine learning. Primary applications have been in turbulence simulation but expanding to  other disciplines.

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. 

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.

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) works on distributed memory and GPU algorithms to accelerate preprocessing and load balancing for applications on current and forthcoming HPC architectures.

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.

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.

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.

Daniel Reynolds (SMU) 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.

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) is interested in developing scalable solution algorithms for linear systems, such as multigrid methods; solvers for advanced architectures; and creating portable HPC scientific software.

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) works on high-performance algorithms for fast Fourier transforms and for solving PDEs on block-structured grids, and their implementations on the latest architectures.

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.

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. 

Victor Paludetto Magri (LLNL) is a computational mathematician and hypre library developer working on HPC, multigrid methods, preconditioning, and linear solvers.

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

Carl Pearson (SNL) works on future architectures for scientific computing, GPU communication for distributed linear algebra, and GPU acceleration of irregular operations.

Siva Rajamanickam (SNL) is interested in high performance computing, sparse linear algebra/solvers, and scientific machine learning.

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.

Ichitaro Yamakazi (SNL) is interested in scalable linear solvers including sparse direct solvers and domain-decomposition based preconditioners.

Hong Zhang (ANL) works on AI for science and high-performance computing.

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.

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. 

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.

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

Toby Isaac (ANL) develops parallel adapive mesh refinement algorithms, numerical methods for implicit PDEs, high performance software for solvers in PETSc/TAO, and tools for inference from large-scale models.

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) works on numerical optimization methods for high-performance architectures and is experienced in nonlinear, constrained, and discrete problems and variational inequalities.

Mauro Perego (SNL)  is a computational mathematician working on discretization and solution of nonlinear partial differential equations, numerical optimization, and scientific machine learning.

Evan Toler (ANL) develops numerical optimization methods for systems governed by partial differential equations. He is particularly interested in magnetic confinement fusion applications.

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.

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) works on uncertainty quantification and machine learning methods for dynamical systems and computational physics applications, including reacting flows, plasma chemistry, and radiation.

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)  initiated the Dakota software project and performs algorithm research in uncertainty quantification (focus on multifidelity methods), surrogate-based optimization, and design under uncertainty.

Gianluca Geracidevelops algorithms for uncertainty quantification and scientific machine learning with a particular emphasis on multi-fidelity methodologies.  (SNL) 

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.

John Jakeman (SNL) develops multi-fidelity algorithms and scientific machine learning (SciML) reduced-order models to accelerate the quantification of uncertainty in high-fidelity simulation models.

Youssef Marzouk (MIT) develops methodologies for uncertainty quantification, Bayesian computation, and machine learning, motivated by a broad range of engineering and science applications.

Cosmin Safta (SNL) develops physics- and data-based algorithms for a large set of applications. 

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.

Emil Constantinescu  (ANL) works on the foundations of AI/ML for scientific computing and UQ for inverse problems. 

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

Miroslav Stoyanov (ORNL) works on surrogate modeling, uncertainty quantification, high-dimensional approximation, and supercomputing. 

Hoang Tran (ORNL) is an applied mathematician specializing in high-dimensional approximation, scientific machine learning and data analysis.