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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...
DSI virtual seminar series debuts on YouTube
May 19, 2021 -
Since launching in 2018, the DSI has hosted more than three dozen speakers in its seminar series. These events invite researchers from academia, industry, and other institutions to discuss their work for an hour to an LLNL audience. In 2020, the series transitioned to a virtual format, and a video playlist of recently recorded seminars is available on the Livermore Lab Events YouTube channel...
Conference papers highlight importance of data security to machine learning
May 12, 2021 -
The 2021 Conference on Computer Vision and Pattern Recognition, the premier conference of its kind, will feature two papers co-authored by an LLNL researcher targeted at improving the understanding of robust machine learning models. Both papers include contributions from LLNL computer scientist Bhavya Kailkhura and examine the importance of data in building models, part of a Lab effort to...
Advanced Data Analytics for Proliferation Detection shares technical advances during two-day meeting
May 7, 2021 -
The Advanced Data Analytics for Proliferation Detection (ADAPD) program held a two-day virtual technical exchange meeting recently. The goal of the meeting was to highlight the science-based and data-driven analysis work conducted by ADAPD to advance the state-of-the-art to accelerate artificial intelligence (AI) innovation and develop AI-enabled systems to enhance the United States’...
A winning strategy for deep neural networks
April 29, 2021 -
LLNL continues to make an impact at top machine learning conferences, even as much of the research staff works remotely during the COVID-19 pandemic. Postdoctoral researcher James Diffenderfer and computer scientist Bhavya Kailkhura, both from LLNL’s Center for Applied Scientific Computing, are co-authors on a paper—“Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural...
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...
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...
Novel deep learning framework for symbolic regression
March 18, 2021 -
LLNL computer scientists have developed a new framework and an accompanying visualization tool that leverages deep reinforcement learning for symbolic regression problems, outperforming baseline methods on benchmark problems. The paper was recently accepted as an oral presentation at the International Conference on Learning Representations (ICLR 2021), one of the top machine learning...
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...
Lawrence Livermore computer scientist heads award-winning computer vision research
Jan. 8, 2021 -
The 2021 IEEE Winter Conference on Applications of Computer Vision (WACV 2021) on Wednesday announced that a paper co-authored by LLNL computer scientist Rushil Anirudh received the conference’s Best Paper Honorable Mention award based on its potential impact to the field. The paper, titled "Generative Patch Priors for Practical Compressive Image Recovery,” introduces a new kind of prior—a...
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...
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...
NeurIPS papers aim to improve understanding and robustness of machine learning algorithms
Dec. 7, 2020 -
The 34th Conference on Neural Information Processing Systems (NeurIPS) is featuring two papers advancing the reliability of deep learning for mission-critical applications at LLNL. The most prestigious machine learning conference in the world, NeurIPS began virtually on Dec. 6. The first paper describes a framework for understanding the effect of properties of training data on the...
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...