What is he best known for?
The fields of study he is best known for:
- Operating system
- Database
- Artificial intelligence
His primary areas of investigation include Distributed computing, Visualization, Supercomputer, Workflow and Petascale computing.
His Distributed computing study integrates concerns from other disciplines, such as Data modeling, Operating system, Data analysis and Memory management.
His Visualization research is multidisciplinary, incorporating perspectives in Scalability, Dataspaces, End user and Software.
The concepts of his Supercomputer study are interwoven with issues in Computer security and Enhanced Data Rates for GSM Evolution.
His studies deal with areas such as Automation, Multi-core processor, Executable and Data management as well as Workflow.
His Petascale computing study also includes
- Partition that intertwine with fields like Parallel computing, Data processing and Input/output,
- NetCDF together with Database.
His most cited work include:
- Terascale direct numerical simulations of turbulent combustion using S3D (420 citations)
- Flexible IO and integration for scientific codes through the adaptable IO system (ADIOS) (235 citations)
- Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks (146 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Distributed computing, Workflow, Visualization, Scalability and Data science.
His studies in Distributed computing integrate themes in fields like Data modeling, Supercomputer, Asynchronous communication, Analytics and Data analysis.
In Workflow, Scott Klasky works on issues like Data management, which are connected to Automation.
As a part of the same scientific study, Scott Klasky usually deals with the Visualization, concentrating on Computational science and frequently concerns with Simulation and Data transmission.
His Scalability research includes elements of Input/output, Software and Parallel computing.
The Data science study combines topics in areas such as Metadata, Petascale computing and Middleware, Database.
He most often published in these fields:
- Distributed computing (29.46%)
- Workflow (24.48%)
- Visualization (18.67%)
What were the highlights of his more recent work (between 2016-2021)?
- Distributed computing (29.46%)
- Supercomputer (9.13%)
- Workflow (24.48%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Distributed computing, Supercomputer, Workflow, Visualization and Data science.
The various areas that Scott Klasky examines in his Distributed computing study include Database-centric architecture, Pipeline and Task.
He combines subjects such as Reduction, Input/output, Server, Software engineering and Computer data storage with his study of Supercomputer.
The study incorporates disciplines such as Scalability, Real-time computing, Data visualization, Software and Big data in addition to Workflow.
Scott Klasky has included themes like Code coupling, World Wide Web and Computational complexity theory in his Visualization study.
His Data science study incorporates themes from Data modeling, Data management, Canopus, Extreme scale and Software portability.
Between 2016 and 2021, his most popular works were:
- Understanding and Modeling Lossy Compression Schemes on HPC Scientific Data (35 citations)
- Predicting Output Performance of a Petascale Supercomputer (27 citations)
- Personalized Search Inspired Fast Interactive Estimation of Distribution Algorithm and Its Application (25 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Database
Scott Klasky focuses on Workflow, Data modeling, Scalability, Distributed computing and Big data.
His Data modeling research is multidisciplinary, incorporating elements of Random access memory, Latency, Persistence, Stacker and Dataspaces.
His Scalability research includes themes of Supercomputer, Server and Memory hierarchy.
His Distributed computing study combines topics from a wide range of disciplines, such as Database-centric architecture and Task.
His biological study spans a wide range of topics, including Software, Data visualization and Data science.
This overview was generated by a machine learning system which analysed the scientist’s
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