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Secretary of Energy Rick Perry Visits Berkeley Lab

March 28, 2018

During his visit to Berkeley Lab's Shyh Wang Hall, Energy Secretary Rick Perry learned about our computational research program, toured NERSC and transferred 500GB of data over ESnet. Read More »

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Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 27, 2018

Petaflop per second deep learning training performance on the Cori supercomputer at NERSC has given climate scientists the ability to use machine learning to identify extreme weather events in huge climate simulation datasets. Read More »

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Underground Neutrino Experiment Could Provide Greater Clarity on Matter-Antimatter Imbalance

March 26, 2018

A new underground neutrino experiment could provide greater clarity on matter-antimatter imbalance in the Cosmos. And NERSC will be the principal site for data processing and analyses throughout the course of the experiment. Read More »

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COSMIC Impact: Next-Gen X-ray Microscopy Platform Now Operational

March 21, 2018

With help from Berkeley Lab’s Center for Advanced Mathematics for Energy Research Applications (CAMERA), the Advanced Light Source's new COSMIC beamline is specialized for studies of active chemistry and electronic properties at tiny scales. Read More »

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A Game Changer: Metagenomic Clustering Powered by HPC

March 12, 2018

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 »

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Combination of Old and New Yields Novel Power Grid Cybersecurity Tool

March 7, 2018

An innovative R&D project led by Berkeley Lab researchers that combines cybersecurity, machine learning 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 »

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Can Strongly Lensed Type Ia Supernovae Resolve One of Cosmology’s Biggest Controversies?

March 1, 2018

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 "microlensing." 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 »

Interested Staff Invited to Apply to Attend 5-Day Machine Learning Hackathon

March 1, 2018

Staff in the lab's Computing Sciences and Biosciences areas are invited to participate in a weeklong workshop focusing on machine learning in data science. The goal of the workshop, to be held April 2-6, is to build bridges between Computing Sciences and Biosciences through a common foundation in statistical computing.   The course is open to all staff in Biosciences and Computing Sciences, but perquisite training in basic Python, basic linear algebra and basic to intermediate statistics is… Read More »

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New Employee Profile- March 2018

March 1, 2018

Vlad Grigorescu, ESnet Vlad Grigorescu, ESnet This month Vlad Grigorescu joins the Energy Sciences Network (ESnet) as a new senior security engineer. In this role he will be writing tools to better detect and investigate security incidents on the network, as well as to help automate some of the responses. Grigorescu has spent his career working in educational and open-source communities, and has contributed to projects like Bro, rsyslog, and Logstash. Before coming to ESnet, Grigorescu was a… Read More »

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Berkeley Lab “Minimalist Machine Learning” Algorithms Analyze Images from Very Little Data

February 20, 2018

Mathematicians at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) 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. Daniël Pelt and James Sethian of he Center for Advanced Mathematics for Energy Research Applications (CAMERA) turned the… Read More »