A.I. and Machine Learning

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September 30, 2020
News Feature
Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.
Daniel Ratner
August 25, 2020
News Feature
Their work uses machine learning to transform the way scientists tune particle accelerators for experiments and solve longstanding mysteries in astrophysics and cosmology.
Portraits of Auralee Edelen and Kimmy Wu
June 23, 2020
News Feature
The prestigious awards provide at least $2.5 million over five years in support of their work in understanding photochemical reactions and improving accelerator beams.
SLAC staff scientists Amy Cordones-Hahn and Brendan O'Shea
April 29, 2020
News Feature
It combines human knowledge and expertise with the speed and efficiency of “smart” computer algorithms.
Accelerator Control Room
April 16, 2020
News Feature
The lab is responding to the coronavirus crisis by imaging disease-related biomolecules, developing standards for reliable coronavirus testing and enabling other essential research.
SARS-CoV-2
June 25, 2019
News Feature
Particle accelerators are some of the most complicated machines in science.
October 16, 2018
News Feature
To keep up with an impending astronomical increase in data about our universe, astrophysicists turn to machine learning.
August 1, 2018
News Feature
Researchers from SLAC and around the world increasingly use machine learning to handle Big Data produced in modern experiments and to study some of the most fundamental properties of the universe.
Machine Learning in HEP
June 21, 2018
News Feature
Tais Gorkhover, Michael Kagan, Kazuhiro Terao and Joshua Turner will each receive $2.5 million for research that studies fundamental particles, nanoscale objects, quantum materials and machine learning.
Photos of SLAC's 2018 Early Career Award winners
April 13, 2018
Press Release
SLAC and its collaborators are transforming the way new materials are discovered. In a new report, they combine artificial intelligence and accelerated experiments to discover potential alternatives to steel in a fraction of the time.
SLAC postdoctoral scholar Fang Ren at an SSRL beamline

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