SCIENCE
Machine Learning (ML) is a common thrust across all science directorates at SLAC. We expect high-level implementation will significantly benefit from collaboration between ML and domain-expert knowledge. Machine learning techniques are actively pursued at SLAC in the following domains.
SSRL

SSRL is a synchrotron x-ray radiation scientific user facility. Scientists visit from all over the world to view the nanoworld, leading to cutting-edge research in drug discovery, energy efficiency and supply, environmental remediation (toxic waste cleanup), electronics, telecommunications and manufacturing.
Accelerator

We design, build, and operate high-performance accelerators used as scientific research instruments. Machine Learning has been applied to accelerator tuning and control, expediting accelerator design optimization, and data analysis and modeling for accelerator diagnostics. Continued development of Machine Learning for accelerators is key to delivering the highest accelerator performance for the user sciences.
Cryogenic Electron Microscope

We enable the study of the building blocks of the cell from its smallest constituents to its largest structures through imaging experiments that produce big data at ever increasing resolution and data rate. Development of Machine Learning for cryoEM data processing and analysis will have a huge impact on our understanding of the molecular basis of life.