Topic: Data Science

In two papers from the 2024 International Conference on Machine Learning, Livermore researchers investigate how LLMs perform under measurable scrutiny.

To keep employees abreast of the latest tools, two data science–focused projects are under way as part of Lawrence Livermore’s Institutional Scientific Capability Portfolio.

The proposed Frontiers in Artificial Intelligence for Science, Security and Technology (FASST) initiative will advance national security; attract and build a talented workforce; harness AI for scientific discovery; address energy challenges; develop technical expertise necessary for AI governance.

This issue highlights some of CASC’s contributions to the DOE's Exascale Computing Project.

LLNL is applying ML to real-world applications on multiple scales. Researchers explain why water filtration, wildfires, and carbon capture are becoming more solvable thanks to groundbreaking data science methodologies on some of the world’s fastest computers.

In a milestone for supercomputing-aided drug design, LLNL and BridgeBio Oncology Therapeutics today announced clinical trials have begun for a first-in-class medication that targets specific genetic mutations implicated in many types of cancer.

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

LLNL’s HPC capabilities play a significant role in international science research and innovation, and Lab researchers have won 10 R&D 100 Awards in the Software–Services category in the past decade.

Two LLNL teams have come up with ingenious solutions to a few of the more vexing difficulties. For their efforts, they’ve won awards coveted by scientists in the technology fields.

Held May 7–8 in Washington, DC, the Special Competitive Studies Project (SCSP) AI Expo showcased groundbreaking initiatives in AI and emerging technologies. Kim Budil and other Lab speakers presented at center stage and the DOE exhibition booth.

In a groundbreaking development for addressing future viral pandemics, a multi-institutional team involving LLNL researchers has successfully combined an AI-backed platform with supercomputing to redesign and restore the effectiveness of antibodies whose ability to fight viruses has been compromi

Throughout the workshop, speakers, panelists and attendees focused on algorithm development, the potential dangers of superhuman AI systems and the importance of understanding and mitigating the risks to humans, as well as urgent measures needed to address the risks both scientifically and politically.

LLNL’s fusion ignition breakthrough, more than 60 years in the making, was enabled by a combination of traditional fusion target design methods, HPC, and AI techniques.

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.

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.

MuyGPs helps complete and forecast the brightness data of objects viewed by Earth-based telescopes.

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.

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

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

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

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.

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

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.