Topic: Data Science

Data researchers, developers, data managers, and program managers from national laboratories visited LLNL to discuss the latest in data management, sharing, and accessibility at the 2023 DOE Data Days (D3) workshop.

LLNL is participating in the 35th annual Supercomputing Conference (SC23), which will be held both virtually and in Denver on November 12–17, 2023.

The Institute of Electrical and Electronics Engineers (IEEE), the world’s largest technical professional organization, has elevated LLNL staff member Bhavya Kailkhura to the grade of senior member within the organization.

Merlin is an open-source workflow orchestration and coordination tool that makes it easy to build, run, and process large-scale workflows.

In recent years, the Lab has boosted its recruiting profile even further by offering the prestigious Sidney Fernbach Postdoctoral Fellowship in the Computing Sciences. The fellowship fosters creative partnerships between new and experienced scientists. In short, it ensures an annual cycle that refreshes advanced research in computer sciences at the Lab.

The Data and Visualization efforts in the DOE’s Exascale Computing Project provide an ecosystem of capabilities for data management, analysis, lossy compression, and visualization.

Cindy Gonzales earned a bachelor’s degree and master’s degree and changed careers—all while working at the Lab. Meet the deputy director of LLNL’s Data Science Institute.

With this year’s results, the Lab has now collected a total of 179 R&D 100 awards since 1978. The awards will be showcased at the 61st R&D 100 black-tie awards gala on Nov. 16 in San Diego.

LLNL's zfp and Variorum software projects are winners. LLNL is a co-developing organization on the winning CANDLE project.

Led by Argonne National Lab and including an LLNL collaborator, a research team aims to provide the security necessary to study life-threatening medical issues without violating patient privacy.

CASC computational mathematician Andrew Gillette has always been drawn to mathematics and says it’s about more than just crunching numbers.

The event brought together 35 University of California students—ranging from undergraduates to graduate-level students from a diversity of majors—to work in groups to solve four key tasks, using actual electrocardiogram data to predict heart health.

Using explainable artificial intelligence techniques can help increase the reach of machine learning applications in materials science, making the process of designing new materials much more efficient.

The Lab’s workhorse visualization tool provides expanded color map features, including for visually impaired users.

This issue highlights some of CASC’s contributions to making controlled laboratory fusion possible at the National Ignition Facility.

The “crystal ball” that provided increased pre-shot confidence in LLNL's fusion ignition breakthrough involved a combination of detailed HPC design and a suite of methods combining physics-based simulation with machine learning—called cognitive simulation, or CogSim.

The report lays out a comprehensive vision for the DOE Office of Science and NNSA to expand their work in scientific use of AI by building on existing strengths in world-leading high performance computing systems and data infrastructure.

Unique among data compressors, zfp is designed to be a compact number format for storing data arrays in-memory in compressed form while still supporting high-speed random access.

The addition of the spatial data flow accelerator into LLNL’s Livermore Computing Center is part of an effort to upgrade the Lab’s cognitive simulation (CogSim) program.

Since 2018, the Lab has seen tremendous growth in its data science community and has invested heavily in related research. Five years later, the Data Science Institute has found its stride.

A novel ML method discovers and predicts key data about networked devices.

Open-source software has played a key role in paving the way for LLNL's ignition breakthrough, and will continue to help push the field forward.

libROM is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).

The prestigious fellow designation is a lifetime honorific title and honors SIAM members who have made outstanding contributions to fields served by the organization.

The new model addresses a problem in simulating RAS behavior, where conventional methods come up short of reaching the time- and length-scales needed to observe biological processes of RAS-related cancers.