What is he best known for?
The fields of study he is best known for:
- Algorithm
- Operating system
- Programming language
His scientific interests lie mostly in Parallel computing, Sparse matrix, Matrix multiplication, Linear algebra and Algorithm.
His Parallel computing study combines topics from a wide range of disciplines, such as Scalability, Computation, ScaLAPACK and Data structure.
His Matrix multiplication research integrates issues from Supercomputer, LU decomposition, Parallel algorithm, Multiplication and QR decomposition.
His Linear algebra research includes elements of Discrete mathematics, Basic Linear Algebra Subprograms and Numerical linear algebra.
His Numerical linear algebra study incorporates themes from Eigenvalues and eigenvectors, System of linear equations and Relaxation.
His work in Algorithm addresses issues such as Linear system, which are connected to fields such as Gaussian elimination and Solver.
His most cited work include:
- Applied Numerical Linear Algebra (2277 citations)
- Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide (999 citations)
- Health monitoring of civil infrastructures using wireless sensor networks (857 citations)
What are the main themes of his work throughout his whole career to date?
James Demmel mainly investigates Parallel computing, Algorithm, Matrix, Matrix multiplication and Linear algebra.
His Parallel computing research integrates issues from Computation and ScaLAPACK.
While the research belongs to areas of Algorithm, James Demmel spends his time largely on the problem of Sparse matrix, intersecting his research to questions surrounding Kernel and Sparse approximation.
His Matrix research incorporates themes from Eigenvalues and eigenvectors and Combinatorics.
His study focuses on the intersection of Matrix multiplication and fields such as Discrete mathematics with connections in the field of Upper and lower bounds.
James Demmel has included themes like Memory hierarchy, Theoretical computer science, Basic Linear Algebra Subprograms and Numerical linear algebra in his Linear algebra study.
He most often published in these fields:
- Parallel computing (25.17%)
- Algorithm (22.86%)
- Matrix (16.40%)
What were the highlights of his more recent work (between 2014-2021)?
- Parallel computing (25.17%)
- Algorithm (22.86%)
- Artificial intelligence (4.85%)
In recent papers he was focusing on the following fields of study:
James Demmel mostly deals with Parallel computing, Algorithm, Artificial intelligence, Speedup and Computation.
His work deals with themes such as Sparse matrix, Krylov subspace, Matrix multiplication and Scaling, which intersect with Parallel computing.
His work in Algorithm tackles topics such as Block which are related to areas like Iterative method.
His research on Speedup also deals with topics like
- Asynchronous communication which is related to area like sync and Solver,
- Scalability, which have a strong connection to Kernel ridge regression.
His research integrates issues of Singular value, Tridiagonal matrix, Bidiagonal matrix, Cluster analysis and Dimensionality reduction in his study of Computation.
His Parallel algorithm study combines topics from a wide range of disciplines, such as Eigenvalues and eigenvectors, Band matrix, QR decomposition, Symmetric matrix and Numerical linear algebra.
Between 2014 and 2021, his most popular works were:
- ImageNet Training in Minutes (202 citations)
- Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (133 citations)
- Solving Sparse Linear Systems with Sparse Backward Error (117 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Algorithm
- Programming language
His primary scientific interests are in Artificial intelligence, Parallel computing, Speedup, Algorithm and Machine learning.
His Parallel computing study frequently links to adjacent areas such as Multigrid method.
His Speedup study combines topics in areas such as Artificial neural network, Simulation, Reduction and Asynchronous communication.
His Algorithm research is multidisciplinary, incorporating elements of Theoretical computer science, Microarchitecture, Cache, Matrix and Mathematical optimization.
His Matrix study often links to related topics such as Computation.
His biological study spans a wide range of topics, including Parallel algorithm, Eigenvalues and eigenvectors, Symmetric matrix and Kernel.
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