Data Science in the News

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Predicting climate change impacts on infrastructure (VIDEO)

Feb. 26, 2024 - 
At LLNL, electrical grid experts and climate scientists work together to bridge the gap between infrastructure and climate modeling. By taking weather variables such as wildfire, flooding, wind, and sunlight that directly impact the electrical grid into consideration, researchers can improve electrical grid model projections for a more stable future. In a new video, LLNL computer scientist...

Machine learning tool fills in the blanks for satellite light curves

Feb. 13, 2024 - 
When viewed from Earth, objects in space are seen at a specific brightness, called apparent magnitude. Over time, ground-based telescopes can track a specific object’s change in brightness. This time-dependent magnitude variation is known as an object’s light curve, and can allow astronomers to infer the object’s size, shape, material, location, and more. Monitoring the light curve of...

Conference paper illuminates neural image compression

Dec. 8, 2023 - 
An enduring question in machine learning (ML) concerns performance: How do we know if a model produces reliable results? The best models have explainable logic and can withstand data perturbations, but performance analysis tools and datasets that will help researchers meaningfully evaluate these models are scarce. A team from LLNL’s Center for Applied Scientific Computing (CASC) is teasing...

Consulting service infuses Lab projects with data science expertise

June 5, 2023 - 
A key advantage of LLNL’s culture of multidisciplinary teamwork is that domain scientists don’t need to be experts in everything. Physicists, chemists, biologists, materials engineers, climate scientists, computer scientists, and other researchers regularly work alongside specialists in other fields to tackle challenging problems. The rise of Big Data across the Lab has led to a demand for...

Data science meets fusion (VIDEO)

May 30, 2023 - 
LLNL’s historic fusion ignition achievement on December 5, 2022, was the first experiment to ever achieve net energy gain from nuclear fusion. However, the experiment’s result was not actually that surprising. A team leveraging data science techniques developed and used a landmark system for teaching artificial intelligence (AI) to incorporate and better account for different variables and...

Scientists develop model for more efficient simulations of protein interactions linked to cancer

March 28, 2023 - 
LLNL scientists have developed a theoretical model for more efficient molecular-level simulations of cell membranes and their lipid-protein interactions, part of a multi-institutional effort to better understand the behavior of cancer-causing membrane proteins. Developed under an ongoing collaboration by the Department of Energy and the National Cancer Institute (NCI) aimed at modeling cell...

LLNL researchers win HPCwire award for applying cognitive simulation to ICF

Nov. 17, 2022 - 
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)

Nov. 17, 2022 - 
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 team claims top AI award at international symbolic regression competition

Aug. 16, 2022 - 
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...

Lab researchers win top award for machine learning-based approach to ICF experiments

Aug. 4, 2022 - 
The IEEE Nuclear and Plasma Sciences Society (NPSS) announced an LLNL team as the winner of its 2022 Transactions on Plasma Science Best Paper Award for their work applying machine learning to inertial confinement fusion (ICF) experiments. In the paper, lead author Kelli Humbird and co-authors propose a novel technique for calibrating ICF experiments by combining machine learning with...

Introduction to deep learning for image classification workshop (VIDEO)

July 6, 2022 - 
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...

Assured and robust…or bust

June 30, 2022 - 
The consequences of a machine learning (ML) error that presents irrelevant advertisements to a group of social media users may seem relatively minor. However, this opacity, combined with the fact that ML systems are nascent and imperfect, makes trusting their accuracy difficult in mission-critical situations, such as recognizing life-or-death risks to military personnel or advancing materials...

Winter hackathon meets WiDS datathon

March 9, 2022 - 
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...

Understanding materials behavior with data science (VIDEO)

Dec. 21, 2021 - 
Computational chemist Rebecca Lindsey, PhD, explains how machine learning and data science techniques are used to develop diagnostic tools for stockpile stewardship, such as models that predict detonator performance. Lindsey also describes how atomistic simulations improve researchers’ understanding of the microscopic phenomena that govern the chemistry in materials under extreme conditions...

Career panel spotlights diversity, equity, and inclusion

Nov. 19, 2021 - 
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...

Building better materials with data science (VIDEO)

Nov. 11, 2021 - 
Research engineer Brian Giera, PhD, describes how data science techniques help collect and analyze data from advanced manufacturing processes in order to craft meaningful experiments. With examples of automated microencapsulation, 3D nanoprinting, metal additive manufacturing, laser track welding, and digital twins, Giera explains how interdisciplinary teams apply machine learning to remove...

Building confidence in materials modeling using statistics

Oct. 31, 2021 - 
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

Oct. 20, 2021 - 
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...

Data Science Challenge welcomes UC Riverside

Oct. 11, 2021 - 
Together with LLNL’s Center for Applied Scientific Computing (CASC), the DSI welcomed a new academic partner to the 2021 Data Science Challenge (DSC) internship program: the University of California (UC) Riverside campus. The intensive program has run for three years with UC Merced, and it tasks undergraduate and graduate students with addressing a real-world scientific problem using data...

Inaugural industry forum inspires ML community

Sept. 16, 2021 - 
LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12. Co-hosted by the Lab’s High-Performance Computing Innovation Center (HPCIC) and Data Science Institute (DSI), the virtual event brought together more than 500 enrollees from the Department of Energy (DOE) complex, commercial companies, professional societies, and academia. Industry sponsors included...