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 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.
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.
We use one of the world's brightest x-ray sources powered by our electron accelerator to take snapshots of atoms at work, revealing how the smallest constituents of our world work. SLAC is leading the way of machine learning at the edge, where fast inference models can help reduce the data load.
High Energy Physics
We study our universe from the smallest constituents to the largest structure through physics experiments that produce big data at ever increasing precision and data rate. Lead by SLAC researchers, Machine Learning has made huge impact in the area of physics simulation data analysis.