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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...
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...
Winter hackathon highlights data science talks and tutorial
March 24, 2021 -
The Data Science Institute (DSI) sponsored LLNL’s 27th hackathon on February 11–12. Held four times a year, these seasonal events bring the computing community together for a 24-hour period where anything goes: Participants can focus on special projects, learn new programming languages, develop skills, dig into challenging tasks, and more. The winter hackathon was the DSI’s second such...
Ana Kupresanin featured in FOE alumni spotlight
March 10, 2021 -
LLNL's Ana Kupresanin, deputy director of the Center for Applied Scientific Computing and member of the Data Science Institute council, was recently featured in a Frontiers of Engineering (FOE) alumni spotlight. Kupresanin develops statistical and machine learning models that incorporate real-world variability and probabilistic behavior to quantify uncertainties in engineering and physics...
'Self-trained' deep learning to improve disease diagnosis
March 4, 2021 -
New work by computer scientists at LLNL and IBM Research on deep learning models to accurately diagnose diseases from X-ray images with less labeled data won the Best Paper award for Computer-Aided Diagnosis at the SPIE Medical Imaging Conference on February 19. The technique, which includes novel regularization and “self-training” strategies, addresses some well-known challenges in the...
Lab researchers explore ‘learn-by-calibration’ approach to deep learning to accurately emulate scientific process
Feb. 10, 2021 -
An LLNL team has developed a “Learn-by-Calibrating” method for creating powerful scientific emulators that could be used as proxies for far more computationally intensive simulators. Researchers found the approach results in high-quality predictive models that are closer to real-world data and better calibrated than previous state-of-the-art methods. The LbC approach is based on interval...
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...
Data Science Institute collaborates with Livermore Lab Foundation and UC Merced to host two year-long fellowships
Dec. 16, 2020 -
Early this spring, the Livermore Lab Foundation (LLF) in partnership with the UC Merced, awarded two rising seniors, Jose Garcia-Esparza and Teagan Zuniga, two one-year $15,000 fellowships to participate in the Lab’s Data Science Summer Institute (DSSI) and continue a part-time fellowship at the Lab for the remainder of the 2020–21 school year.
The fellowships allow deserving students...
After successful virtual summer, DSSI looks ahead to 2021
Dec. 14, 2020 -
In a year distinguished by the COVID-19 pandemic, LLNL’s Data Science Summer Institute (DSSI) pivoted quickly to an all-virtual program. The 28 students in the class of 2020 worked from their homes and attended online seminars, one-on-one mentoring sessions, team-building games, and other activities. Instead of an in-person poster session, the students participated in a virtual Summer Slam...
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...
LLNL to provide supercomputing resources to universities selected by NNSA Advanced Simulation and Computing program
Oct. 2, 2020 -
LLNL will provide significant computing resources to students and faculty from nine universities that were newly selected for participation in the National Nuclear Security Administration (NNSA)’s Predictive Science Academic Alliance Program (PSAAP). The program is funded by NNSA’s Office of Advanced Simulation and Computing (ASC) program. The primary focus of this third phrase of PSAAP will...
Machine learning speeds up and enhances physics calculations
Oct. 1, 2020 -
Interpreting data from NIF’s cutting-edge high energy density science experiments relies on physics calculations that are so complex they can challenge LLNL supercomputers, which stand among the best in the world. A collaboration between LLNL and French researchers found a novel way to incorporate machine learning and neural networks to significantly speed up inertial confinement fusion...