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Meeting 2016 TMS Annual Meeting & Exhibition
Symposium Driving Discovery: Integration of Multi-Modal Imaging and Data Analysis
Presentation Title Recognizing Patterns from Experimental Data
Author(s) Daniela Ushizima
On-Site Speaker (Planned) Daniela Ushizima
Abstract Scope Research across a myriad of science domains is increasingly reliant on image-based data from experiments. The challenge is to analyze the data torrent generated by these experiments in a timely manner and provide insights such as measurements for decision-making. Our goal is to construct software tools that help scientists uncover relevant, but hidden, information in digital images. In collaboration with colleagues at DOE-BES/ASCR and UCB-BIDS, we have exploited the scientific value of a broad array of high resolution, multidimensional datasets. This multi-disciplinary work is designed around a coordinated research effort connecting (1) state-of-the-art data analysis methods with basis on pattern recognition and machine learning; (2) emerging algorithms for dealing with massive datasets; and (3) advances in evolving computer architectures. These advances will accelerate the analyses of image-based recordings, scaling scientific procedures by reducing time between experiments, increasing efficiency, and opening more opportunities for more users of the imaging facilities.
Proceedings Inclusion? Planned: A print-only volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

3D and 4D Characterization of Failure Mechanisms in Commercial Li-Ion Batteries
Bingham Mixture Model for Efficient Microtexture Estimation from Discrete Orientation Data
Correlation of Multi-modal Chemical Imaging with Computational Simulations for Energy Materials
Digital Representation of Materials Grain Structure from Four-Dimensional X-ray Microtomography Data
Error Analysis of Near-field High Energy Diffraction Microscopy
In Situ Synchrotron Quantification of Evolving Solidification Microstructures in Ni and Co Based Alloys
Integrated Imaging: The Sum is Greater than the Parts
Integrated Multimodal Imaging of Cathodes for Lithium Ion Battery
Methodology for Reconstruction of Samples Analyzed with Simultaneous Neutron and X-Ray Imaging
Modeling Multi-modal Images of Photocatalysis on Cu2O
Multi-Modality Imaging at the Hard X-ray Nanoprobe Beamline at the NSLS-II
Multi-scale, Multi-Model Analysis of Deformation Behavior in Metallic Materials by X-ray Microtomography, FIB, and EBSD
Neutrons, Materials and Data Challenges
Real Time Analysis, Interpretation and Experimental Steering for Electron Microscopy
Recognizing Patterns from Experimental Data
Structure Quantification, Property Prediction and 4D Reconstruction Using Limited X-ray Tomography Data

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