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Chad A. Steed
Researcher at Oak Ridge National Laboratory
Publications - 61
Citations - 819
Chad A. Steed is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Visual analytics & Visualization. The author has an hindex of 14, co-authored 60 publications receiving 707 citations. Previous affiliations of Chad A. Steed include United States Naval Research Laboratory & Battelle Memorial Institute.
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Journal ArticleDOI
Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets.
Alex Belianinov,Rama K. Vasudevan,Evgheni Strelcov,Chad A. Steed,Sang Mo Yang,Alexander Tselev,Stephen Jesse,Michael D. Biegalski,Galen M. Shipman,Christopher T. Symons,Albina Y. Borisevich,Rick Archibald,Sergei V. Kalinin +12 more
TL;DR: Here, several recent applications of the big and deep data analysis methods are reviewed to visualize, compress, and translate this multidimensional structural and functional data into physically and chemically relevant information.
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Big data visual analytics for exploratory earth system simulation analysis
Chad A. Steed,Daniel M. Ricciuto,Galen M. Shipman,Brian E. Smith,Peter E. Thornton,Dali Wang,Xiaoying Shi,Dean N. Williams +7 more
TL;DR: This paper describes and demonstrates a visual analytics system, called the Exploratory Data analysis ENvironment (EDEN), with specific application to the analysis of complex earth system simulation data sets, and bridges the growing gap between viable visualization techniques and real-world climate analysis.
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Situ: Identifying and Explaining Suspicious Behavior in Networks
John R. Goodall,Eric D. Ragan,Chad A. Steed,Joel W. Reed,G. David Richardson,Kelly M. T. Huffer,Robert A. Bridges,Jason A. Laska +7 more
TL;DR: Situ provides a scalable solution that combines anomaly detection with information visualization that enables operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous.
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Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing ☆ ☆☆
TL;DR: A visual analytics approach is proposed that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers to discover patterns related to defects and system performance issues.
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Ultrascale Visualization of Climate Data
Dean N. Williams,T. Bremer,Charles Doutriaux,John Patchett,Sean Williams,Galen M. Shipman,Ross Miller,Dave Pugmire,Brian E. Smith,Chad A. Steed,E. W. Bethel,Hank Childs,Harinarayan Krishnan,Prabhat Prabhat,Michael Wehner,Cláudio T. Silva,Emanuele Santos,David Koop,Tommy Ellqvist,Jorge Poco,Berk Geveci,Aashish Chaudhary,Andrew Bauer,A. Pletzer,D. Kindig,Gerald L. Potter,Thomas Maxwell +26 more
TL;DR: Collaboration across research, government, academic, and private sectors is integrating more than 70 scientific computing libraries and applications through a tailorable provenance framework, empowering scientists to exchange and examine data in novel ways.