SLAC Experts

Daniel Ratner

Machine Learning Department Head

Areas of research: New technologies; machine learning; deep learning; artificial intelligence; accelerator physics; physics and machine learning

Machine learning is changing every facet of what we do at SLAC. It gives us new ideas for how to design and run experiments, and it lets us look at more challenging data and ask tougher physics questions.

Media contact Machine learning website

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AI is playing a key role in helping SLAC researchers find new galaxies and tiny neutrinos, and discover new drugs.

$Diffraction pattern$

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Researchers across the lab are developing AI tools to harness data and particle beams in real time and make molecular movies, speeding up the...

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The method could lead to the development of new materials with tailored properties, with potential applications in fields such as climate change, quantum computing...

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

Wan-Lin Hu is seen talking with talks with accelerator systems operator Kabir Lubana in the lab’s main Accelerator Control Room.

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The Horizon Prizes celebrate the most exciting, contemporary chemical science at the cutting edge of research and innovation.

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Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.

Daniel Ratner

Dark matter, neutrinos and drug discovery: how AI is powering SLAC science and technology

Taming big data and particle beams: how SLAC researchers are pushing AI to the edge

New AI approach accelerates targeted materials discovery and sets the stage for self-driving experiments

A day in the life of a human-in-the-loop engineer

Molecular movie pioneers awarded the Royal Society of Chemistry's new Horizon Prize

Q&A: How machine learning helps scientists hunt for particles, wrangle floppy proteins and speed discovery

Selected stories featuring Daniel Ratner