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This seminar series focuses on scientific computation for APS experiments. The series focuses on advanced software and computing infrastructure for analysis, reduction, reconstruction, and simulation. It provides an opportunity to learn about state-of-the-art computational techniques and tools and how they are being applied to science at the APS. It will start with talks from Argonne staff who are working on projects in collaboration or in support of APS science. APS Scientific Computation Seminar Series Home |
| Introducing Parsl: A Parallel Scripting Library for Python - December 20, 2017 |
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Kyle Chard, Senior Researcher and Fellow in the Computation Institute at the University of Chicago and Argonne National Laboratory Abstract (pdf) |
| Materials Data Facility - Streamlined and Automated Data Sharing, Discovery, Access, and Analysis - April 17, 2017 |
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Ben Blaiszik, Computation Institute, The University of Chicago Abstract (pdf) |
| Leveraging First Principles Modeling for X-Ray Data Inversion - February 20, 2017 |
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Maria K. Y. Chan, Nanoscience & Technology Division (NST), Argonne National Laboratory & Computation Institute, The University of Chicago Abstract (pdf) |
| Deep Neural Networks for Synchrotron X-Ray Imaging - January 16, 2017 |
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Xiaogang Yang, X-Ray Science Division (XSD), Argonne National Laboratory Abstract (pdf) |