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Virtual LLNL-UC Merced Data Science Challenge tackles asteroid detection though machine learning
Over three weeks, students from the University of California, Merced collaborated online with mentors at Lawrence Livermore National Laboratory (LLNL) to tackle a real-world challenge problem: using machine learning to identify potentially hazardous asteroids that could pose an existential threat to humanity. The Data Science Challenge was the third such annual event for…
Lawrence Livermore team looks at nuclear weapon effects for near-surface detonations
A Lawrence Livermore National Laboratory (LLNL) team has taken a closer look at how nuclear weapon blasts close to the Earth’s surface create complications in their effects and apparent yields. Attempts to correlate data from events with low heights of burst revealed a need to improve the theoretical treatment of strong blast waves rebounding from hard surfaces. This led…
Lawrence Livermore team looks at nuclear weapon effects for near-surface detonations
A Lawrence Livermore National Laboratory (LLNL) team has taken a closer look at how nuclear weapon blasts close to the Earth’s surface create complications in their effects and apparent yields. Attempts to correlate data from events with low heights of burst revealed a need to improve the theoretical treatment of strong blast waves rebounding from hard surfaces. This led…
Lab postdocs invited to prestigious Heidelberg Forum
Two Lawrence Livermore National Laboratory (LLNL) postdoctoral researchers are among a select group of 200 scientists invited to attend the 8thHeidelberg Laureate Forum, an international conference that connects young researchers with laureates of the major prizes in mathematics and computer science. For a week in September, LLNL computational engineering postdocs Ruben…
Conference papers highlight importance of data security to machine learning
The 2021 Conference on Computer Vision and Pattern Recognition, the premier conference of its kind, will feature two papers co-authored by a Lawrence Livermore National Laboratory (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…
Students build knowledge of machinist trade during Lab's first-ever virtual Manufacturing Workshop
The COVID-19 pandemic didn’t prevent local high school students from learning what it’s like to be one of the more than 150 machinists who work at Lawrence Livermore National Laboratory (LLNL) during the Materials Engineering Division’s (MED) Manufacturing Workshop, held April 20-22. Students attended the three-day workshop virtually after their school days ended, where…
Advanced Data Analytics for Proliferation Detection shares technical advances during two-day meeting
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…
Lawrence Livermore takes part in international planetary defense conference
Ten scientists from Lawrence Livermore National Laboratory (LLNL) last week took part in the 7th IAA Planetary Defense Conference (PDC), hosted by the United Nations Office for Outer Space Affairs in cooperation with the European Space Agency. Megan Bruck Syal, who helped lead the Lab’s participation in the event and who also was a conference session chair, said this year…
HPC4Energy Innovation kicks off spring solicitation
The Department of Energy’s High Performance Computing for Energy Innovation (HPC4EI) Initiative is accepting industry proposals for projects leveraging the world-class supercomputing and expertise of DOE national laboratories to address key energy-related challenges in domestic manufacturing. The DOE Office of Energy Efficiency and Renewable Energy’s Advanced Manufacturing…
Krell Institute honors Hittinger with Corones Award
The Krell Institute, a nonprofit organization serving the scientific and educational communities, has awarded Lawrence Livermore National Laboratory (LLNL) computational scientist Jeff Hittinger with its 2021 James Corones Award in Leadership, Community Building and Communication. The award, named for the institute’s founder, recognizes mid-career scientists and engineers…
LLNL, IBM and Red Hat joining forces to explore standardized HPC resource management interface
Lawrence Livermore National Laboratory (LLNL), IBM and Red Hat are combining forces to develop best practices for interfacing high performance computing (HPC) schedulers and cloud orchestrators, an effort designed to prepare for emerging supercomputers that take advantage of cloud technologies. Under a recently signed memorandum of understanding (MOU), researchers aim to…
Lab offers forum on machine learning for industry
Lawrence Livermore National Laboratory (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. Pre-registrations are open for the forum, which aims to foster and illustrate the adoption of machine learning methods for…
De Supinski named one of HPCwire’s 'People to Watch'
Bronis R. de Supinski, Lawrence Livermore National Laboratory’s (LLNL) chief technology officer (CTO) for Livermore Computing (LC), is one of the top influencers in the high performance computing industry for 2021, according to HPCwire. On April 7, the publication honored de Supinski as one of its "People to Watch," a group of 14 “innovators and visionaries building and…
COVID-19 HPC Consortium reflects on past year
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…
Novel deep learning framework for symbolic regression
Lawrence Livermore National Laboratory (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…
Lab event encourages growth of women in data science
Coinciding with International Women’s Day on March 8, Lawrence Livermore National Laboratory’s 4th Women in Data Science (WiDS) regional event brought women together to discuss successes, opportunities and challenges of being female in a mostly male field. The Lab’s first-ever virtual WiDS gathering attracted dozens of LLNL data scientists as well as some from outside the…
Research uncovers missing physics in explosive hotspots
Research conducted on Lawrence Livermore National Laboratory’s (LLNL) supercomputer Quartz highlights findings made by scientists that reveal a missing aspect of the physics of hotspots in TATB (1,3,5-trimamino-2,4,6-trinitrobenzene) and other explosives. Hotspots are localized regions of elevated temperature that form from shock-induced collapse of microstructural…
'Self-trained' deep learning to improve disease diagnosis
New work by computer scientists at Lawrence Livermore National Laboratory (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 Feb. 19. The technique, which includes novel regularization and “self-training”…
Retiring Director Bill Goldstein leaves behind a rich legacy of extraordinary growth, innovation for the Lab
Nearly a year into piloting a major scientific institution through one of the most taxing and disruptive global events in modern history, outgoing Livermore Lab Director Bill Goldstein is ready for a vacation. One of Goldstein’s first orders of business following his retirement on March 1 is returning to the lush slopes, coffee plantations and sandy beaches of Kona, Hawaii…
Lab researchers explore ‘learn-by-calibration’ approach to deep learning to accurately emulate scientific process
Lawrence Livermore National Laboratory (LLNL) computer scientists have developed a new deep learning approach to designing emulators for scientific processes that is more accurate and efficient than existing methods. In a paper published by Nature Communications, an LLNL team describes a “Learn-by-Calibrating” (LbC) method for creating powerful scientific emulators that…
