SI and machine learning

The prototype DUNE 2x2 detector will capture up to 10,000 neutrino interactions per day.

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.

Charging lithium-ion batteries at high currents just before they leave the factory is 30 times faster and increases battery lifespans by 50%, according to a study at the SLAC-Stanford Battery Center. 

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.

Improvements to the lab’s “electron camera” use AI and “time stamping” to help reveal nature’s speedy processes more accurately. 

The SLAC/Stanford researcher is a leading materials scientist and entrepreneur whose research is paving the way for better batteries, cleaner power grids.

Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it?

Physics may seem like its own world, but different sectors using machine learning are all part of the same universe. 

Scientists inside and outside of particle physics and astrophysics are leaning on AI for assistance with complex tasks.

In the coming weeks, Symmetry will explore the ways scientists are using artificial intelligence to advance particle physics and astrophysics—in a series of articles written and illustrated entirely by humans.

Wan-Lin Hu’s job is to improve the way people and artificial intelligence collaborate to run SLAC’s complex machines.