Data Science in the News

Supercomputing’s critical role in the fusion ignition breakthrough

On December 5th, the research team at LLNL's National Ignition Facility (NIF) achieved a historic win in energy science: for the first time ever, more energy was produced by an artificial fusion reaction than was consumed—3.15 megajoules produced versus 2.05 megajoules in laser energy to cause the reaction. High-performance computing was key to this breakthrough (called ignition), and HPCwire...

National Ignition Facility achieves fusion ignition

The U.S. Department of Energy (DOE) and DOE’s National Nuclear Security Administration (NNSA) today announced the achievement of fusion ignition at LLNL—a major scientific breakthrough decades in the making that will pave the way for advancements in national defense and the future of clean power. On Dec. 5, a team at LLNL’s National Ignition Facility (NIF) conducted the first controlled...

LLNL staff returns to Texas-sized Supercomputing Conference

The 2022 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22) returned to Dallas as a large contingent of LLNL staff participated in sessions, panels, paper presentations, and workshops centered around HPC. The world’s largest conference of its kind celebrated its highest in-person attendance since the start of the COVID-19 pandemic, with about 11...

Understanding the universe with applied statistics (VIDEO)

In a new video posted to the Lab’s YouTube channel, statistician Amanda Muyskens describes MuyGPs, her team’s innovative and computationally efficient Gaussian Process hyperparameter estimation method for large data. The method has been applied to space-based image classification and released for open-source use in the Python package MuyGPyS. MuyGPs will help astronomers and astrophysicists...

LLNL researchers win HPCwire award for applying cognitive simulation to ICF

The high performance computing publication HPCwire announced LLNL as the winner of its Editor’s Choice award for Best Use of HPC in Energy for applying cognitive simulation (CogSim) methods to inertial confinement fusion (ICF) research. The award was presented at the largest supercomputing conference in the world: the 2022 International Conference for High Performance Computing, Networking...

ESGF launches effort to upgrade climate projection data system

The Earth System Grid Federation (ESGF), a multi-agency initiative that gathers and distributes data for top-tier projections of the Earth’s climate, is preparing a series of upgrades that will make using the data easier and faster while improving how the information is curated. The federation, led by the Department of Energy’s Oak Ridge National Laboratory in collaboration with Argonne and...

Scientific discovery for stockpile stewardship

Among the significant scientific discoveries that have helped ensure the reliability of the nation’s nuclear stockpile is the advancement of cognitive simulation. In cognitive simulation, researchers are developing AI/ML algorithms and software to retrain part of this model on the experimental data itself. The result is a model that “knows the best of both worlds,” says Brian Spears, a...

Lab directors discuss LLNL’s past, present and future at ‘historic conversation’

On a historic occasion in the Livermore wine country, the nine living LLNL directors gathered on Sept. 8 to mark the Laboratory’s 70th anniversary, share stories and discuss their vision for the Lab in the coming years. Hosted by the nonprofit Livermore Lab Foundation, the panel brought past directors John Foster Jr., John Nuckolls, Bruce Tarter, George Miller, Parney Albright, and Bill...

Celebrating 10 years of hackathons

Hackathons are one of LLNL Computing’s most enduring and beloved traditions. Although some details have changed since the first hackathon, the premise remains the same: Participants have 24 hours to work on any project of their choosing, whether that’s learning a programming language, building a prototype, developing a new skill, or experimenting with a software framework. They are encouraged...

LLNL team claims top AI award at international symbolic regression competition

An LLNL team claimed a top prize at an inaugural international symbolic regression competition for an artificial intelligence (AI) framework they developed capable of explaining and interpreting real-life COVID-19 data. Hosted by the open source SRBench project at the 2022 Genetic and Evolutionary Computation Conference (GECCO), the competition had two tracks—synthetic and real-world—and...

Panel discussion spotlights COVID-19 R&D

The DSI’s career panel series continued on June 28 to highlight some of LLNL’s COVID-19 research projects. Three data scientists—Emilia Grzesiak, Derek Jones, and Priyadip Ray—joined moderator and data scientist Stewart He to talk about their work in drug screening, protein–drug compounds, antibody–antigen sequence analysis, and risk factor identification. He, who earned a PhD in Computer...

Introduction to deep learning for image classification workshop (VIDEO)

In addition to its annual conference held every March, the global Women in Data Science (WiDS) organization hosts workshops and other activities year-round to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field. On June 29, LLNL’s Cindy Gonzales led a WiDS Workshop titled “Introduction to Deep Learning for Image Classification.” The abstract...

Kevin McLoughlin applies computational biology to complex problems

Kevin McLoughlin has always been fascinated by the intersection of computing and biology. His LLNL career encompasses award-winning microbial detection technology, a COVID-19 antiviral drug design pipeline, and work with the ATOM consortium. The appeal for him in these projects lies at the intersection of computing and biology. “I love finding ways to visualize data that reveal relationships...

Livermore WiDS provides forum for women in data science

LLNL celebrated the 2022 Global Women in Data Science (WiDS) conference on March 7 with its fifth annual regional event, featuring workshops, mentoring sessions and a discussion with LLNL Director Kim Budil, the first woman to hold that role. For the second straight year, the WiDS Livermore event was entirely virtual due to the COVID-19 pandemic. Attendees tuned in to view talks and...

Winter hackathon meets WiDS datathon

Sponsored by the DSI, LLNL’s winter hackathon took place on February 16–17. Hackathons are 24-hour events that encourage collaborative programming and creative problem solving. In addition to traditional hacking, the hackathon included a special datathon competition in anticipation of the Women in Data Science (WiDS) conference on March 7. Hackathon and datathon participants presented their...

WiDS Livermore returns on March 7

We are hosting our 5th WiDS Livermore regional event to encourage our community of women in computing. We will watch the WiDS Stanford Livestream as well as feature Lab-focused technical talks, mentoring breakout sessions, and a career panel. WiDS Livermore is an independent event hosted by LLNL Ambassadors as part of the annual Women in Data Science (WiDS) Worldwide conference organized by...

Career panel spotlights diversity, equity, and inclusion

The DSI’s career panel series continued on November 3 with a session highlighting diversity, equity, and inclusion (DEI) as well as the Lab’s DEI-focused employee resource groups (ERGs). ERGs are sponsored by LLNL’s Office of Strategic Diversity and Inclusion Programs. Moderator Anh Quach, member of the Asian Pacific American Council (APAC), was joined by four panelists: Raul Viera Mercado...

LLNL-led team uses machine learning to derive black hole motion from gravitational waves

To understand the motion of binary black holes, researchers have traditionally simplified Einstein’s field equations and solved them to calculate the emitted gravitational waves. The approach is complex and requires expensive, time-consuming simulations on supercomputers or approximation techniques that can lead to errors or break down when applied to more complicated black hole systems. Alo...

Summer scholar develops data-driven approaches to key NIF diagnostics

Su-Ann Chong's summer project, “A Data-Driven Approach Towards NIF Neutron Time-of-Flight Diagnostics Using Machine Learning and Bayesian Inference,” is aimed at presenting a different take on nToF diagnostics. Neutron time-of-flight diagnostics are an essential tool to diagnose the implosion dynamics of inertial confinement fusion experiments at NIF, the world’s largest and most energetic...

Tackling the COVID-19 pandemic

To help the U.S. fight the COVID-19 pandemic, LLNL did what it does best: quickly bring together interdisciplinary teams and diverse technologies to address urgent national challenges. This effort includes applying advanced high-performance computing resources to biological research and anayzing complicated computer models and enormous datasets. Read more in Science & Technology Review.