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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 |
| A Machine-Learning Based Stochastic Algorithm for Design and Online Optimization - November 16, 2020 |
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Xiaobiao Huang, APS, Argonne National Laboratory Abstract (pdf) |
| Data-Driven Approaches to Accelerated Discovery of Complex and Metastable Materials - November 2, 2020 |
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Apurva Mehta, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory Abstract (pdf) |
| Tomviz: Data Analysis, Visualization and Reproducibility - October 19, 2020 |
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Marcus Hanwell, Computational Scientist, Data Acquisition, Management and Analysis Group Abstract (pdf) |
| X-ray 3D Imaging – Experiment Workflow for Automation and Reproducibility - September 21, 2020 |
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Francesco De Carlo, APS, Argonne National Laboratory Abstract (pdf) |
| FPGA Acceleration for Data Scientists and Network Engineers - August 17, 2020 |
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Yatish Kumar, ESnet Affiliate Abstract (pdf) |
| FPGA Acceleration for Data Scientists and Network Engineers - August 17, 2020 |
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Yatish Kumar, ESnet Affiliate Abstract (pdf) |
| Analysis of Synchrotron Extended X-ray Absorption Fine Structure (EXAFS) Data Using Artificial Intelligence - July 20, 2020 |
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Jeff Terry, Illinois Institute of Technology Abstract (pdf) |