Dr. Daniel Ratner

ZOOM Meeting and On-Site at the DESY Auditorium (Registration Necessary)

October 21st, 2021

5 pm

Chair: Dr. Thomas J. Lane

Machine Learning for X-Ray and cryoEM at SLAC

Within the US Department of Energy’s national lab system, the wealth of data, access to robust automation, and requirements for control, simulation, and data acquisition, make “big science” experiments — telescopes, particle accelerators, etc. — ideal targets for machine learning.  In this talk I will describe SLAC National Accelerator Laboratory’s approach to machine learning applications for x-ray and cryogenic electron microscopy (cryo-EM) experiments.  I will highlight several examples in autonomous optimization of accelerators, analysis and acquisition of x-ray free-electron laser data, and single-particle imaging from cryo-EM experiments.

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