AI is playing a key role in helping SLAC researchers find new galaxies and tiny neutrinos, and discover new drugs.

Researchers across the lab are developing AI tools to harness data and particle beams in real time and make molecular movies, speeding up the discovery process in the era of big data.

SLAC is leading an effort to build a network that will enable AI and machine learning to steer experiments and more.

Digital design engineer Abhilasha Dave’s passion for connecting machine learning and hardware is helping SLAC solve big data challenges.

David Cesar, Julia Gonski and W.L. Kimmy Wu will each receive $2.75 million issued over five years for their research in X-ray and ultrafast science, new physics and primordial gravitational waves.

The method could lead to the development of new materials with tailored properties, with potential applications in fields such as climate change, quantum computing and drug design.

SLAC works with two small businesses to make its ACE3P software easier to use in supercomputer simulations for optimizing the shapes of accelerator structures.

An extension of the Stanford Research Computing Facility will host several data centers to handle the unprecedented data streams that will be produced by a new generation of scientific projects.

The leaders of SLAC's Technology Innovation Directorate discuss how their group supports the lab's most innovative projects.

They discover a short-lived state that could lead to faster and more energy-efficient computing devices.

Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.

Two projects will look for ways to link individual quantum devices into networks for quantum computing and ultrasensitive detectors.