With increasingly better instruments, we can now see things at atomic scales, measure vibrations imperceptible to the human eye, and capture high-resolution images of objects millions of light-years away. But those instruments are also producing vastly more data than ever before. Machine learning methods tailored to scientific data provide powerful tools for analyzing these complex datasets, as well as controlling scientific experiments.
Berkeley Lab researchers have been addressing these challenges for the last several years, and from August to September 2020, we featured some of their projects and postdocs in a social media campaign. Here’s a collection of that content. For more on machine learning for science research at Berkeley Lab visit: https://ml4sci.lbl.gov/.
Introduction
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Steve Farrell: ML for High Energy Physics Q+A |
Profile: Peter Harrington |
ML for Covid-19 Research |
ML for Traffic Prediction |
AR1K: ML for Agriculture |
Profile: Yan Zhang |
Karthik Kashinath: ML for Earth Systems Modeling Q+A |
Profile: Nicole Sanderson |
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ML for Cosmology |
ML4Sci Summer Students |
Profile: Jaideep Pathak |
ML For Climate and Weather |
Profile: Brandon Wood |
ML for Monitoring Groundwater |
Profile: Daniel Murname |
ML for Biofuels |
About Computing Sciences at Berkeley Lab
High performance computing plays a critical role in scientific discovery. Researchers increasingly rely on advances in computer science, mathematics, computational science, data science, and large-scale computing and networking to increase our understanding of ourselves, our planet, and our universe. Berkeley Lab's Computing Sciences Area researches, develops, and deploys new foundations, tools, and technologies to meet these needs and to advance research across a broad range of scientific disciplines.
Please be aware that this historical content might mention programs, people, and research that aren’t currently active at Berkeley Lab, links to web pages that don’t work anymore, or documents that aren’t available. We’ve preserved this information just the way it was, so that others can learn more about the past.
