Data Science in the News

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Inaugural industry forum inspires ML community

Sept. 16, 2021 - 
LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12. Co-hosted by the Lab’s High-Performance Computing Innovation Center (HPCIC) and Data Science Institute (DSI), the virtual event brought together more than 500 enrollees from the Department of Energy (DOE) complex, commercial companies, professional societies, and academia. Industry sponsors included...

Visualization software stands the test of time

Sept. 13, 2021 - 
In the decades since LLNL’s founding, the technology used in pursuit of the Laboratory’s national security mission has changed over time. For example, studying scientific phenomena and predicting their behaviors require increasingly robust, high-resolution simulations. These crucial tasks compound the demands on high-performance computing hardware and software, which must continually be...

60 years of cancer research

Sept. 10, 2021 - 
From studying radioactive isotope effects to better understanding cancer metastasis, the Laboratory’s relationship with cancer research endures some 60 years after it began, with historical precedent underpinning exciting new research areas. In one Cancer Moonshot project, research includes a close synergy between experiments and computation, allowing scientists to get a better picture of the...

New machine-learning tactic sharpens NIF shot predictions

July 8, 2021 - 
Inertial confinement fusion (ICF) experiments at LLNL's National Ignition Facility (NIF) are extremely complex and costly, and it is challenging to accurately and consistently predict the outcome. But that is now changing, thanks to the work of design physicists. In a paper recently published in Physics of Plasmas, design physicist Kelli Humbird and her colleagues describe a new machine...

Virtual LLNL-UC Merced Data Science Challenge tackles asteroid detection though machine learning

June 25, 2021 - 
Over three weeks, students from the University of California, Merced collaborated online with mentors at LLNL to tackle a real-world challenge problem: using machine learning to identify potentially hazardous asteroids that could pose an existential threat to humanity. Throughout the event, the teams tackled problems around the theme of “Astronomy for Planetary Defense.” For the main...

COVID-19 detection and analysis with Nisha Mulakken (VIDEO)

June 7, 2021 - 
LLNL biostatistician Nisha Mulakken has enhanced the Lawrence Livermore Microbial Detection Array (LLMDA) system with detection capability for all variants of SARS-CoV-2. The technology detects a broad range of organisms—viruses, bacteria, archaea, protozoa, and fungi—and has demonstrated novel species identification for human health, animal health, biodefense, and environmental sampling...

Laser-driven ion acceleration with deep learning

May 25, 2021 - 
While advances in machine learning over the past decade have made significant impacts in applications such as image classification, natural language processing and pattern recognition, scientific endeavors have only just begun to leverage this technology. This is most notable in processing large quantities of data from experiments. Research conducted at LLNL is the first to apply neural...

The data-driven future of extreme physics

May 19, 2021 - 
By applying modern machine learning and data science methods to “extreme” plasma physics, researchers can gain insight into our universe and find clues about creating a limitless amount of energy. In a recent perspective published in Nature, LLNL scientists and international collaborators outline key challenges and future directions in using machine learning and other data-driven techniques...

Lab offers forum on machine learning for industry

April 22, 2021 - 
LLNL is looking for participants and attendees from industry, research institutions and academia for the first-ever Machine Learning for Industry Forum (ML4I), a three-day virtual event starting Aug. 10. The event is sponsored by LLNL’s High Performance Computing Innovation Center and the Data Science Institute. The deadline for submitting presentations or industry use cases is June 30. The...

Virtual seminar series explores data-driven physical simulations

April 6, 2021 - 
The rapidly growing fields of artificial intelligence (AI) and machine learning (ML) have become cornerstones of LLNL’s data science research activities. The Lab’s scientific community regularly publishes advancements in both AI/ML applications and theory, contributing to international discourse on the possibilities of these compelling technologies. The large volume of AI/ML scientific...

COVID-19 HPC Consortium reflects on past year

April 1, 2021 - 
COVID-19 HPC Consortium scientists and stakeholders met virtually on March 23 to mark the consortium’s one-year anniversary, discussing the progress of research projects and the need to pursue a broader organization to mobilize supercomputing access for future crises. The White House announced the launch of the public-private consortium, which provides COVID-19 researchers with free access to...

ATOM Consortium welcomes 3 DOE national labs to accelerate drug discovery

March 29, 2021 - 
The Accelerating Therapeutics for Opportunities in Medicine (ATOM) consortium, of which LLNL is part, announced the U.S. Department of Energy’s Argonne, Brookhaven and Oak Ridge national laboratories are joining the consortium to further develop ATOM’s AI-driven drug discovery platform. The public-private ATOM consortium aims to transform drug discovery from a slow, sequential and high-risk...

CASC research in machine learning robustness debuts at AAAI conference

Feb. 10, 2021 - 
LLNL’s Center for Applied Scientific Computing (CASC) has steadily grown its reputation in the artificial intelligence (AI)/machine learning (ML) community—a trend continued by three papers accepted at the 35th AAAI Conference on Artificial Intelligence, held virtually on February 2–9, 2021. Computer scientists Jayaraman Thiagarajan, Rushil Anirudh, Bhavya Kailkhura, and Peer-Timo Bremer led...

LLNL physicist wins Young Former Student award

Dec. 16, 2020 - 
Texas A&M University’s Department of Nuclear Engineering on December 10 announced it has honored LLNL physicist Kelli Humbird with its 2020-21 Young Former Student award for her work at LLNL in combining machine learning with inertial confinement fusion (ICF) research. Humbird graduated from Texas A&M with a PhD in nuclear engineering in 2019. Since joining the Laboratory as an intern in 2016...

Model for COVID-19 drug discovery a Gordon Bell finalist

Nov. 17, 2020 - 
A machine learning model developed by a team of LLNL scientists to aid in COVID-19 drug discovery efforts is a finalist for the Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research. Using Sierra, the world’s third fastest supercomputer, LLNL scientists produced a more accurate and efficient generative model to enable COVID-19 researchers to produce novel compounds...

DOE announces five new energy projects at LLNL

Nov. 13, 2020 - 
The DOE today announced two rounds of awards for the High Performance Computing for Energy Innovation Program HPC4EI), including five projects at LLNL. HPC4EI connects industry with the computational resources and expertise of the DOE national laboratories to solve challenges in manufacturing, accelerate discovery and adoption of new materials and improve energy efficiency. The awards were...

What put LLNL at the center of U.S. supercomputing in 2020?

Nov. 12, 2020 - 
The HPC world is waiting for the next series of transitions to far larger machines with exascale capabilities. By this time next year, the bi-annual ranking of the Top500 most powerful systems will be refreshed at the top as Frontier, El Capitan, Aurora, and other DOE systems come online. While LLNL was already planning around AI acceleration for its cognitive simulation aims and had a number...

From intern to mentor, Nisha Mulakken builds a career in bioinformatics

Nov. 3, 2020 - 
The COVID-19 pandemic has sparked a wave of new research and development at the Lab, and Nisha Mulakken is very busy. The biostatistician has enhanced the Lawrence Livermore Microbial Detection Array (LLMDA) system with detection capability for all variants of SARS-CoV-2. The technology detects a broad range of organisms—viruses, bacteria, archaea, protozoa, and fungi—and has demonstrated...

AI gets a boost via LLNL, SambaNova collaboration

Oct. 20, 2020 - 
LLNL has installed a state-of-the-art artificial intelligence (AI) accelerator from SambaNova Systems, the National Nuclear Security Administration (NNSA) announced today, allowing researchers to more effectively combine AI and machine learning (ML) with complex scientific workloads. LLNL has begun integrating the new AI hardware, SambaNova Systems DataScale™, into the NNSA’s Corona...

LLNL, ANL and GSK provide early glimpse into Cerebras AI system performance

Oct. 13, 2020 - 
AI chip and systems startup Cerebras was one of many AI companies showcased at the AI Hardware Summit which concluded last week. Cerebras invited collaborators from LLNL, Argonne National Laboratory, and GlaxoSmithKline to talk about their early work on Cerebras machines and future plans. Livermore Computing's CTO Bronis de Supinski said, “We have this vision for performing cognitive...