Data Science in the News

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...

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...

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...

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...

LLNL to cooperate with University of Utah's one oneAPI Center of Excellence

The University of Utah has announced the creation of a new oneAPI Center of Excellence focused on developing portable, scalable and performant data compression techniques. The oneAPI Center will be headed out of the University of Utah’s Center for Extreme Data Management Analysis and Visualization (CEDMAV) and will involve the cooperation of LLNL’s Center for Applied Scientific Computing. It...

An open-source, data-science toolkit for energy: GridDS

As the number of smart meters and the demand for energy is expected to increase by 50% by 2050, so will the amount of data those smart meters produce. While energy standards have enabled large-scale data collection and storage, maximizing this data to mitigate costs and consumer demand has been an ongoing focus of energy research. An LLNL team has developed GridDS—an open-source, data-science...

Defending U.S. critical infrastructure from nation-state cyberattacks

For many years, LLNL has been conducting research on cybersecurity, as well as defending its systems and networks from cyberattacks. The Lab has developed an array of capabilities to detect and defend against cyberintruders targeting IT networks and worked with government agencies and private-sector partners to share its cybersecurity knowledge to the wider cyberdefense community. LLNL has...

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...

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...

Building confidence in materials modeling using statistics

LLNL statisticians, computational modelers, and materials scientists have been developing a statistical framework for researchers to better assess the relationship between model uncertainties and experimental data. The Livermore-developed statistical framework is intended to assess sources of uncertainty in strength model input, recommend new experiments to reduce those sources of uncertainty...

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...

Career panel series kicks off with women in Computing leadership roles

More than 100 LLNL staff and students gathered virtually for the first session of a new career panel series inspired by the annual WiDS conference and sponsored by the DSI. Panelists discussed how they have shaped their careers at the Lab and in Computing, their journeys into leadership roles, and how they navigate career challenges. Data scientist and panel series organizer Cindy Gonzales...

Laser-driven ion acceleration with deep learning

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

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 event encourages growth of women in data science

Coinciding with International Women’s Day on March 8, LLNL’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 Lab, and featured speakers, a career panel and...

Ana Kupresanin featured in FOE alumni spotlight

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...

DOE announces five new energy projects at LLNL

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...