News
Mueller’s Career Path a Blueprint for Other Researchers
Finding a career can be an exciting and sometimes challenging time in one’s life. It involves being noticed by your peers and making connections to find people to work with. This can be intimidating for some but comes naturally to others, like researcher Juliane Mueller. Read More »
COSMIC Impact: Next-Gen X-ray Microscopy Platform Now Operational
With help from Berkeley Lab’s Center for Advanced Mathematics for Energy Research Applications (CAMERA), the Advanced Light Source's new COSMIC (Coherent Scattering and Microscopy) beamline is specialized for studies of active chemistry and electronic properties at tiny scales. Read More »
A Game Changer: Metagenomic Clustering Powered by HPC
A team of researchers from Berkeley Lab's CRD and JGI took one of the most popular clustering approaches in modern biology—the Markov Clustering algorithm—and modified it to run quickly, efficiently and at scale on distributed-memory supercomputers. Read More »
Combination of Old and New Yields Novel Power Grid Cybersecurity Tool
An R&D project led by Berkeley Lab researchers that combines cybersecurity, machine learning algorithms and commercially available power system sensor technology to better protect the electric power grid has sparked interest from U.S. utilities, power companies and government officials. Read More »
Can Strongly Lensed Type Ia Supernovae Resolve One of Cosmology’s Biggest Controversies?
Using the SciDAC developed SEDONA code and NERSC supercomputers, astrophysicists at Berkeley Lab and the University of Portsmouth discovered how to control the effects of "micolensing." Armed with this knowledge they believe they will be able to find 1000 strongly lensed Type Ia supernovae in real-time from LSST data--that's 20 times more than previous expectations. Read More »
Berkeley Lab “Minimalist Machine Learning” Algorithms Analyze Images from Very Little Data
Berkeley Lab mathematicians have developed a new approach to machine learning aimed at experimental imaging data. Rather than relying on the tens or hundreds of thousands of images used by typical machine learning methods, this new approach “learns” much more quickly and requires far fewer images. Read More »
Delivering Efficient Parallel I/O with HDF5 on Exascale Computing Systems
In an interview with Department of Energy's ECP communications team, Berkeley Lab's Suren Byna and Quincey Koziol talk about delivering efficient parallel I/O with HDF5 on exascale computing systems. Read More »
Berkeley Lab Researchers Contribute to Making Blockchains Even More Robust
In the last few years, researchers at Berkeley Lab, UC Davis and University of Stavanger in Norway have developed a new protocol, called BChain, which makes private blockchain even more robust. The researchers are also working with colleagues at Berkeley Lab and beyond to adapt this tool to support applications that are of strategic importance to the Department of Energy’s Office of Science. Read More »
Berkeley Lab’s ArrayUDF Tool Turns Large-scale Scientific Array Data Analysis Into a Cakewalk
A novel scalable framework developed by CRD researchers is improving scientific productivity by allowing researchers to run user-defined custom analysis operations on large arrays of data with massively parallel supercomputers, while leaving complex data management and performance optimization tasks up to the underlying system. Read More »
Beta of Neurodata Without Borders Software Now Available
Neuroscientists can now explore a beta version of the new Neurodata Without Borders: Neurophysiology (NWB:N 2.0) software and offer input to developers before it is fully released next year. Read More »
