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

APS Artificial Intelligence/Machine Learning (AI/ML) Workshop

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Start Time
9:00 a.m.

Artificial Intelligence/Machine Learning (AI/ML) hold great potential to impact the work performed at the APS. There are many exciting activities already underway, and many more are being planned. Now is a great time to hold a workshop focused on AI/ML activities at the APS to meet with our colleagues, talk about current projects, discuss tools, exchange ideas and experiences, and inspire collaboration.
The APS AI/ML workshop will be held over two sessions: Tuesday, January 21, 2020 from 9 AM to Noon, and Friday, January 24, 2020 from 9 AM to Noon. Both sessions will be in Building 401 Room A1100. 

Barbara Frosik, Nicholas Schwarz, Alec Sandy


9:00 Machine Learning Enabled Advanced X-ray Spectroscopy in the APS-U Era
Sun Chengjun, Maria Chan, Elise Jennings, Steve Heald, Xiaoyi Zhang
9:15 Integrating AI and Simulations for X-ray Data Interpretation
Maria Chan
9:30 Real-time Coherent Diffraction Inversion Through Deep Learning
Henry Chan, Mathew Cherukara, Ross Harder
9:45 Automatic Differentiation for 3D Bragg Ptychographic Reconstruction
Tao Zhou, Mathew Cherukara, Martin Holt
10:00 Applications of the DLHub Learning System to Problems in Microscopy and Spectroscopy
Marcus Schwarting, Logan Ward, Ryan Chard, CD Phatak, Tiberiu Stan,
Zachary Thompson, Peter Voorhees, Ben Blaiszik, Ian Foster
10:15 Break/Group Photo
10:45 Cyberinfrastructure for Autonomous Science
Ryan Chard, Ben Blaiszik, Ian Foster
11:00 Identifying and Separating Components of Heterogeneous Materials with Machine Learning
Logan Ward, Marcus Schwarting
11:15 Data Science for In Situ Synthesis
Wenqian Xu, Uta Ruett
11:30 Machine Learning for Identification and Removal of Spurious Data
Kenley Pelzer, Brian Toby, Ross Harder
11:45 Application of Artificial Neural Network in the APS Linac Bunch Charge Transmission Efficiency
Hairong Shang, Yine Sun
Noon PDF studies of disordered materials using Machine Learning
Chris Benmore, Ganesh Sivaraman, Alvaro Vazquez-Mayagoitia
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