What is he best known for?
The fields of study he is best known for:
- Operating system
- Algorithm
- Statistics
His scientific interests lie mostly in Parallel computing, Algorithm, Combinatorics, Cluster analysis and Lanczos algorithm.
His Parallel computing study integrates concerns from other disciplines, such as Kernel and Computational science.
Horst D. Simon works mostly in the field of Algorithm, limiting it down to topics relating to Eigenvalues and eigenvectors and, in certain cases, Stability and Maxima and minima.
His Combinatorics research incorporates elements of Discrete mathematics, QR decomposition and Gramian matrix.
The various areas that Horst D. Simon examines in his Cluster analysis study include Adjacency matrix, Matrix and Graph partition.
His Supercomputer research is multidisciplinary, incorporating elements of Unified Parallel C and Massively parallel.
His most cited work include:
- The Nas Parallel Benchmarks (2023 citations)
- Partitioning sparse matrices with eigenvectors of graphs (1448 citations)
- A min-max cut algorithm for graph partitioning and data clustering (733 citations)
What are the main themes of his work throughout his whole career to date?
Horst D. Simon spends much of his time researching Supercomputer, Parallel computing, Computational science, Algorithm and Eigenvalues and eigenvectors.
His TOP500 study in the realm of Supercomputer interacts with subjects such as National laboratory.
Horst D. Simon specializes in Parallel computing, namely Massively parallel.
His Computational science research incorporates themes from Parallel algorithm, Parallel processing, Visualization, Software and MIMD.
Horst D. Simon interconnects Dimension, Sparse matrix, Laplacian matrix and Cluster analysis in the investigation of issues within Algorithm.
As a part of the same scientific study, Horst D. Simon usually deals with the Eigenvalues and eigenvectors, concentrating on Applied mathematics and frequently concerns with Mathematical optimization.
He most often published in these fields:
- Supercomputer (29.21%)
- Parallel computing (25.84%)
- Computational science (14.04%)
What were the highlights of his more recent work (between 2006-2020)?
- Supercomputer (29.21%)
- Parallel computing (25.84%)
- Data science (7.30%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Supercomputer, Parallel computing, Data science, Futures contract and Massively parallel.
His Supercomputer study combines topics from a wide range of disciplines, such as Emerging technologies and Computer engineering.
His work deals with themes such as Solver, Data structure and Fortran, which intersect with Parallel computing.
His Data science study combines topics in areas such as Field, Computing Methodologies and TOP500.
His work carried out in the field of Massively parallel brings together such families of science as Load balancing, Theoretical computer science, Distributed memory and Partition.
His research in Algorithm tackles topics such as Hermitian matrix which are related to areas like Mathematical optimization.
Between 2006 and 2020, his most popular works were:
- The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses (230 citations)
- Compass: a scalable simulator for an architecture for cognitive computing (103 citations)
- A learning framework using Green's function and kernel regularization with application to recommender system (50 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Algorithm
- Statistics
Horst D. Simon mostly deals with Supercomputer, Parallel computing, Mathematical optimization, Data science and Massively parallel.
His research integrates issues of Computing Methodologies, Work in process and FLOPS in his study of Supercomputer.
His study brings together the fields of Computation and Parallel computing.
His Mathematical optimization research focuses on subjects like Algorithm, which are linked to Hermitian matrix, Eigenvalues and eigenvectors, Lanczos resampling and Semi-supervised learning.
He has researched Data science in several fields, including Data-driven, Computer architecture, TOP500 and Metric.
His studies in Massively parallel integrate themes in fields like Scalability and Theoretical computer science.
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