Topic: Data Science

With over 90 people in attendance, including those attending online and in person, the WiDS Livermore conference was once again successful in facilitating the exchange of information and fresh ideas.

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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.

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The Lab is hosting two related WiDS events: First is a datathon on February 28, then the annual regional conference on March 13. These hybrid events are free and open to everyone.

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New research reveals subtleties in the performance of neural image compression methods, offering insights toward improving these models for real-world applications.

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A record number of attendees—more than 14,000—experts, researchers, vendors and enthusiasts in the field of HPC descended on the Mile High City for the 2023 International Conference for High Performance Computing, Networking, Storage and Analysis, colloquially known as SC23.

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LLNL researchers collaborated with Washington University in St. Louis to devise a state-of-the-art, machine learning ML–based reconstruction tool for when high-quality computed tomography data is in low supply.

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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.

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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.

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

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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.

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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.

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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.

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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.

People Highlight

zfp is an open-source C/C++ library for compressed floating-point and integer arrays that support high throughput read and write random access.

Project

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

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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.

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

People Highlight

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.

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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.

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

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

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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.

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