SLAC People

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September 1, 2021
News Feature
Three physicists talk about how they got started, their work at SLAC and what they would say to others considering a career in STEM.
Isleydys Silva Torrecilla, Emmanuel Aneke and Bhavna Nayak
June 3, 2021
News Feature
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.
Panofsky fellows
May 27, 2021
News Feature
Edward Hohenstein, Emma McBride and Caterina Vernieri study what happens to molecules hit by light, recreate extreme states of matter like those inside stars and planets, and search for new physics phenomena at the most fundamental level.
Early Career Awardees 2021
April 23, 2021
News Feature
External
Zhenan Bao, Axel Brunger and Robert Byer are among 252 new members elected to the society, which honors exceptional scholars, leaders, artists and innovators engaged in advancing the public good.
AAAS new members
March 4, 2021
News Feature
Overseeing the safe disposal of chemicals and waste at SLAC has turned into an environmental mission for Yolanda Pilastro.
Yolanda Pilastro portrait at SLAC
October 21, 2020
News Feature
At the Machine Shop, Pete Franco crafts beautiful, intricate and precise parts for the lab’s latest scientific tools.
Pete Franco at the SLAC Machine Shop
October 5, 2020
External
Cryan is an investigator with the Stanford PULSE Institute at SLAC, while Marsden is an associate professor of pediatrics and of bioengineering at Stanford.
Portrait of James Cryan and Alison Marsden
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
September 24, 2020
News Brief
Institute of Electrical and Electronics Engineers recognizes his contributions to developing electron beams that power unique ‘electron cameras’ and could advance X-ray lasers.
Xijie Wang
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

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