Machine Learning for Science

Applying Decision Science to Transportation Behaviors

March 12, 2020

Why do some people adopt electric vehicles, ride-sharing, ride-hailing, or online shopping while others don’t? Berkeley Lab researcher Anna Spurlock spearheads the WholeTraveler Transportation Behavior Study, a three-year project that uses machine learning techniques to try to understand what drives human choices in transportation. >>Read the story.

AI for Science: The Big Picture




A Powerful Scientific Tool

Machine learning is a branch of artificial intelligence that makes inferences from raw data using sophisticated algorithms and powerful computers. For online shoppers, that means better "you might also like..." suggestions. But for scientists, machine learning tools can reveal profound insights hiding in ballooning datasets.

Thanks to better instruments, including technologies developed at Berkeley Lab, we can see things at a microscopic and atomic scale, measure vibrations imperceptible to the human eye, and capture high-resolution images of objects millions of light years away. But those instruments produce vastly larger datasets than ever. The Large Synoptic Survey Telescope (LSST) will produce 20 terabytes of data every night, about 60 petabytes over its lifetime. The Large Hadron Collider has already produced 900 petabytes of data (50 petabytes in 2018 alone) and expects to create another 500 petabytes by 2024. Conventional data analysis alone can't keep up.

Using machine learning techniques, models can be automatically derived from that data. These models can be used to identify features, reduce complexity, and control experiments.

Solving Problems, Advancing Science

Using learning technologies in over 100 different projects, Berkeley Lab scientists track atomic particles, search for better battery materials, analyze traffic patterns, improve crop yields, pinpoint extreme weather in exascale climate simulations, and piece together metagenomic puzzles from billions of DNA fragments.

With five DOE national user facilities (for nanotechnology science, high-performance computing, synchrotron x-ray research, networking and genomics) world class applied mathematics, computer and computational science, and a pool of scientific talent that has produced 13 Nobel laureates, scientific machine learning has found fertile ground at the lab.

We are using learning technologies to...

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Math, Software & Tools to Spur Innovation

Berkeley Lab's research into machine learning builds on its foundational work in mathematics to develop methods that are are consistent with physical laws, robust in the presence of noisy or biased data, and capable of being interpreted and explained in scientifically meaningful ways.

As a Department of Energy National Laboratory, we develop and share the algorithms, software, tools and libraries that are foundational to scientific machine learning. We gather, organize and store huge scientific datasets in areas such as materials, energy, environment, biology, genomics, and astronomy. And we develop tools and advanced networking facilities to make these datasets more searchable and accessible.