The algorithm pairs machine-learning techniques with classical beam physics equations to avoid massive data crunching.

Public lecture presented by Axel Levy.

Presented by Axel Levy.

A machine learning algorithm automatically extracts information to speed up – and extend – the study of materials with X-ray pulse pairs.

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.

Knowing a magnet’s past will allow scientists to customize particle beams more precisely in the future. As accelerators stretch for higher levels of performance, understanding subtle effects, such as those introduced by magnetic history, is becoming more critical.

SLAC’s Matt Garrett and Susan Simpkins talk about tech transfer that brings innovations from the national lab to the people, including advances for medical devices and self-driving vehicles.

Teaching machine learning the basics of accelerator physics is particularly useful in situations where actual data don’t exist.

She toured the lab’s powerful X-ray laser, looked at the construction of the world’s largest digital camera, and discussed climate research, industries of the future, and diversity, equity and inclusion in the sciences.

The lab will help fund the work of researchers who use artificial intelligence and machine learning to make energy systems more sustainable, affordable, resilient and fair to all socioeconomic groups.

From the invisible world of elementary particles to the mysteries of the cosmos, recipients of this prestigious award for early career scientists explore nature at every level.