A.I. and Machine Learning

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September 30, 2020
News Feature
Q&A: How machine learning helps scientists hunt for particles, wrangle floppy proteins and speed discovery
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
Auralee Edelen and Wai Ling Wu receive 2020 Panofsky Fellowships at SLAC
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
SLAC’s Amy Cordones-Hahn and Brendan O’Shea receive DOE Early Career Awards
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
A new machine learning method streamlines particle accelerator operations
It combines human knowledge and expertise with the speed and efficiency of “smart” computer algorithms.
Accelerator Control Room
April 16, 2020
News Feature
SLAC joins the global fight against COVID-19
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
Symmetry: The future of particle accelerators may be autonomous
Particle accelerators are some of the most complicated machines in science.
October 16, 2018
News Feature
Symmetry: Studying the stars with machine learning
To keep up with an impending astronomical increase in data about our universe, astrophysicists turn to machine learning.
August 1, 2018
News Feature
Particle physicists team up with AI to solve toughest science problems
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
Four SLAC Scientists Awarded Prestigious DOE Early Career Research Grants
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
Scientists Use Machine Learning to Speed Discovery of Metallic Glass
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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