One of the many unanswered scientific questions about COVID-19 is whether it is seasonal like the flu – waning in warm summer months then resurging in the fall and winter. Now scientists at Lawrence Berkeley National Laboratory are launching a project to apply machine-learning methods to a plethora of health and environmental datasets, combined with high-resolution climate models and seasonal forecasts, to tease out the answer.
A team of materials scientists at Berkeley Lab – scientists who normally spend their time researching things like high-performance materials for thermoelectrics or battery cathodes – have built a text-mining tool in record time to help the global scientific community synthesize the mountain of scientific literature on COVID-19 being generated every day.
In a machine learning challenge dubbed the 2020 Large Hadron Collider Olympics, a team of cosmologists from Berkeley Lab developed a code that best identified a mock signal hidden in simulated particle-collision data.
A team of researchers at Berkeley Lab and UC Berkeley has successfully demonstrated how machine-learning tools can improve the stability of light beams’ size for science experiments at a synchrotron light source via adjustments that largely cancel out unwanted fluctuations.
Berkeley Lab computer scientists are working with Caltrans to use high performance computing and machine learning to help improve real-time decision making when traffic incidents occur.
Researchers have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. A Berkeley Lab-led team collected 3.3 million abstracts of published materials science papers and fed them into an algorithm. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials.
A team of researchers from Berkeley Lab and UC Berkeley are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via a web-based search engine for scientific data, called Science Search, that the Berkeley team is building.
How will the farms of the future feed a projected 9.8 billion people by 2050? Berkeley Lab’s “smart farm” project marries microbiology and machine learning in an effort to reduce the need for chemical fertilizers and enhance soil carbon uptake, thus improving the long-term viability of the land while increasing crop yields.
While it may be the era of supercomputers and “big data,” without smart methods to mine all that data, it’s only so much digital detritus. Now researchers at Lawrence Berkeley National Laboratory and UC Berkeley have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time.
Berkeley Lab mathematicians have developed a new approach to machine learning aimed at experimental imaging data. Rather than relying on the thousands of images used by typical machine learning methods, this new approach “learns” much more quickly and requires far fewer images.