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Machine Learning for Science Campaign

September 17, 2020

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

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

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.