The Advanced Photon Source
a U.S. Department of Energy Office of Science User Facility

APS Scientific Computation Seminar Series - 2022

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

Automatic Parameter Tuning for High-Resolution Ptychography

Yi Jiang, Beamline Data Scientist

X-Ray Science Division, Argonne National Laboratory

Abstract (pdf)

APS Ptychography Software

Daniel Ching, Assistant Computational Scientist and Steve Henke, Data Engineer;

X-Ray Science Division, Argonne National Laboratory

Abstract (pdf)

A Scalable, Real-Time Machine Vision Platform for Microscopy

Christopher R. Field, Theia Scientific LLC, Ann Arbor, Michigan

Abstract (pdf)

In-situ TEM of the Radiation Effects on Material Microstructures in IVEM-Tandem Facility: Overview and Recent Development

Wei-Ying Chen, Materials Scientist, Nuclear Science and Engineering Division, Argonne National Laboratory

Abstract (pdf)

Real-Time Reduction of Time-of-Flight Neutron Diffraction Data at the SNS

Christopher Fancher and Malcolm Guthrie, Oak Ridge National Laboratory

Abstract (pdf)

Distributed Data and Workflow Management at JGI

Kjiersten Fagnan, Chief Informatics Officer, Joint Genome Institute, Lawrence Berkeley National Laboratory

Abstract (pdf)

Data-guided, Multi-scale, and High-dimensional Understanding of the Battery Degradation 

Yijin Liu, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory

Abstract (pdf)

TomopyUI: A User-Friendly Tool for Rapid Tomography Alignment and Reconstruction 

Samuel S. Welborn, Ph.D.

Abstract (pdf)

Deep Learning Based X-ray CT Reconstruction for Fast and High-Quality Characterization in Metal Additive Manufacturing Leveraging CAD Models and Physics-Based Information - April 25, 2022

Amir Koushyar Ziabari, R&D Staff Scientist, Oak Ridge National Laboratory

Abstract (pdf)

Machine Learning Phasing for Bragg Coherent Diffractive Imaging - April 11, 2022

Ian Robinson, Brookhaven National Laboratory and University College London
Shinjae Yoo, Brookhaven National Laboratory
Longlong Wu, Brookhaven National Laboratory

Abstract (pdf)

New Characterization Methods for Crystalline Materials at Fourth-Generation Coherent Light Sources - March 28, 2022

Siddharth Maddali, Argonne National Laboratory

Abstract (pdf)

X-Dart: A GUI Tool for XRD Data Visualization and Analysis Based on pyFAI and pyGIX  - March 14, 2022

Vivek Thampy, SLAC National Accelerator Laboratory

Abstract (pdf)

Coded-Apertures for Depth-Resolved X-ray Laue Microdiffraction - February 14, 2022

Doga Gursoy, Argonne National Laboratory

Abstract (pdf)

Modeling PDF data from liquids: What’s next? - January 31, 2022

Chris Benmore, Argonne National Laboratory

Abstract (pdf)