Data Science in the News

DSI Consulting Service spurs innovation

Today, research in nearly every scientific discipline involves data science techniques. Whether using sophisticated tools to manage and analyze massive datasets or applying machine learning algorithms to gain new insights, such techniques are becoming ever more prevalent. However, scientists and engineers may not have specific training in the newest, most pertinent data science and...

El Capitan verified as world's fastest supercomputer

LLNL, in collaboration with the National Nuclear Security Administration (NNSA), Hewlett Packard Enterprise and AMD, have officially unveiled El Capitan as the world's most powerful supercomputer and first exascale system dedicated to national security. Verified at 1.742 exaflops (1.742 quintillion calculations per second) on the High Performance Linpack—the standard benchmark used by the...

ICECap looks to use exascale fusion simulations to pioneer digital design

A groundbreaking multidisciplinary team of LLNL researchers is combining the power of exascale computing with AI, advanced workflows and graphics processor (GPU)-acceleration to advance scientific innovation and revolutionize digital design. The project, called ICECap (Inertial Confinement on El Capitan), is a transformative approach to inertial confinement fusion (ICF) design optimization...

DOE honors seven early-career Lab scientists

Seven LLNL scientists are recipients of the DOE's Office of Science Early Career Research Program (ECRP) award. Among them is Shusen Liu, a computer scientist in the Machine Intelligence Group in the Center for Applied Scientific Computing. His work focuses on understanding and interpreting the inner mechanisms of neural networks and integrating human domain knowledge with machine...

Measuring attack vulnerability in AI/ML models

LLNL is advancing the safety of AI/ML models in materials design, bioresilience, cyber security, stockpile surveillance, and many other areas. A key line of inquiry is model robustness, or how well it defends against adversarial attacks. A paper accepted to the renowned 2024 International Conference on Machine Learning explores this issue in detail. In “Adversarial Robustness Limits via...

LLNL researchers unleash machine learning in designing advanced lattice structures

Characterized by their intricate patterns and hierarchical designs, lattice structures hold immense potential for revolutionizing industries ranging from aerospace to biomedical engineering, due to their versatility and customizability. However, the complexity of these structures and the vast design space they encompass have posed significant hurdles for engineers and scientists, and...

International workshop focuses on AI for critical infrastructure

On August 4, LLNL researchers Felipe Leno da Silva and Ruben Glatt hosted the AI for Critical Infrastructure workshop at the 33rd International Joint Conference on Artificial Intelligence (IJCAI) in Jeju, South Korea. Professors Wencong Su (University of Michigan – Dearborn) and Yi Wang (University of Hong Kong) joined them in organizing the workshop focused on exploring AI opportunities and...

Evaluating trust and safety of large language models

Accepted to the 2024 International Conference on Machine Learning, two Livermore papers examined trustworthiness—how a model uses data and makes decisions—of large language models, or LLMs. In “TrustLLM: Trustworthiness in Large Language Models,” Bhavya Kailkhura and collaborators from universities and research organizations around the world developed a comprehensive trustworthiness...

Department of Energy announces FASST initiative

On July 16, the Department of Energy formally announced the proposed Frontiers in Artificial Intelligence for Science, Security and Technology (FASST) initiative via the web page www.energy.gov/fasst (with accompanying video and fact sheet). As stated on the web page, the speed and scale of the AI landscape are significant motivators for investing in strategic AI capabilities: “Without FASST...

AI, fusion, and national security with Brian Spears (VIDEO)

This episode of the Eye on AI podcast delves into the cutting-edge world of AI and high-performance computing with Brian Spears, director of LLNL's AI Innovation Incubator. The episode is presented here as a video with the following description: "Brian shares his experience in driving AI into national security science and managing the nation’s nuclear stockpile. With a PhD in mechanical...

Signal and image science community comes together for annual workshop

Nearly 150 members of the signal and image science community recently came together to discuss the latest advances in the field and connect with colleagues, friends, and potential collaborators at the 28th annual Center for Advanced Signal and Image Science (CASIS) workshop. The event featured more than 50 technical contributions across six workshop tracks and a parallel tutorials session...

LLNL and BridgeBio announce trials for supercomputing-discovered cancer drug

In a substantial milestone for supercomputing-aided drug design, LLNL and BridgeBio Oncology Therapeutics (BridgeBio) today announced clinical trials have begun for a first-in-class medication that targets specific genetic mutations implicated in many types of cancer. The development of the new drug—BBO-8520—is the result of collaboration among LLNL, BridgeBio and the National Cancer...

FAA awards approval for drone swarm testing

LLNL’s Autonomous Sensors team has received the Federal Aviation Administration’s (FAA’s) first and—to date—only certificate of authorization allowing autonomous drone swarming exercises on the Lab main campus. These flights will test swarm controls and sensor payloads used in a variety of national security applications. Autonomous drone swarms differ from those used for entertainment...

Harnessing the power of AI for a safe and secure future (VIDEO)

LLNL, alongside the Department of Energy’s (DOE’s) 17 national labs, is harnessing the transformative potential of AI for a safer, more secure future. In 2022, LLNL made history by achieving fusion ignition, marking a pivotal moment for national security and clean energy. While AI continues to unlock new insights into fusion, through the combination of cutting-edge computer modeling...

Manufacturing optimized designs for high explosives

When materials are subjected to extreme environments, they face the risk of mixing together. This mixing may result in hydrodynamic instabilities, yielding undesirable side effects. Such instabilities present a grand challenge across multiple disciplines, especially in astrophysics, combustion, and shaped charges—a device used to focus the energy of a detonating explosive, thereby creating a...

Accelerating material characterization: Machine learning meets X-ray absorption spectroscopy

LLNL scientists have developed a new approach that can rapidly predict the structure and chemical composition of heterogeneous materials. In a new study in ACS Chemistry of Materials, Wonseok Jeong and Tuan Anh Pham developed a new approach that combines machine learning with X-ray absorption spectroscopy (XANES) to elucidate the chemical speciation of amorphous carbon nitrides. The research...

Will it bend? Reinforcement learning optimizes metamaterials

Lawrence Livermore staff scientist Xiaoxing Xia collaborated with the Technical University of Denmark to integrate machine learning (ML) and 3D printing techniques. The effort naturally follows Xia’s PhD work in materials science at the California Institute of Technology, where he investigated electrochemically reconfigurable structures. In a paper published in the Journal of Materials...

Conference paper illuminates neural image compression

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...

For better CT images, new deep learning tool helps fill in the blanks

At a hospital, an airport, or even an assembly line, computed tomography (CT) allows us to investigate the otherwise inaccessible interiors of objects without laying a finger on them. To perform CT, x-rays first shine through an object, interacting with the different materials and structures inside. Then, the x-rays emerge on the other side, casting a projection of their interactions onto a...

Lab partners with new Space Force Lab

LLNL subject matter experts have been selected by the U.S. Space Force to help stand up its newest Tools, Applications, and Processing (TAP) laboratory dedicated to advancing military space domain awareness (SDA). The Livermore team attended the October 26 kickoff in Colorado Springs of the SDA TAP lab’s Project Apollo technology accelerator, designed with an open framework to support and...