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...

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...

Data Science Challenge sees summer surge

Lawrence Livermore welcomed students from four institutions for this year’s Data Science Challenge (DSC) internship program. Hosted by the DSI, the DSC gives undergraduate and graduate students a taste of the multidisciplinary research performed at national laboratories. In addition to UC Merced and UC Riverside, participants hailed from two new partnering institutions: Case Western Reserve...

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...

The surprising places you’ll find machine learning (VIDEO)

LLNL data scientists are applying ML to real-world applications on multiple scales. A new DSI-funded video highlights research at the nanoscale (developing better water treatment methods by predicting the behavior of water molecules under the extremely confined conditions of nanotubes); mesoscale (determining the likelihood and location of a dangerous wildfire-causing phenomenon called arcing...

DOE, LLNL take center stage at inaugural AI expo

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. LLNL is rapidly expanding research investments to build transformative AI-driven solutions to critical national security challenges. While developing...

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...

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...

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...

Igniting scientific discovery with AI and supercomputing (VIDEO)

LLNL’s fusion ignition breakthrough, more than 60 years in the making, was enabled by a combination of traditional fusion target design methods, high-performance computing (HPC), and AI techniques. The success of ignition marks a significant milestone in fusion energy research, and was facilitated in part by the precision simulations and rapid experimental data analysis only possible through...

Welcome new DSI team members

When Data Science Institute (DSI) director Brian Giera and deputy director Cindy Gonzales began planning activities for fiscal year 2024 and beyond, they immediately realized that LLNL’s growth in data science and artificial intelligence (AI)/machine learning (ML) research requires corresponding growth in the DSI’s efforts. “Our field is booming,” Giera states. “The Lab has a stake in the...

WiDS Livermore conference attendees network, share research and absorb wisdom

Co-sponsored by the DSI, LLNL on March 13 hosted the 7th annual Women in Data Science (WiDS) conference for data scientists, industry professionals, recent graduates and others interested in the field. As an independent satellite of the global WiDS conference celebrating International Women’s Day, the Livermore hybrid event was held to highlight the work and careers of LLNL and regional data...

Predicting climate change impacts on infrastructure (VIDEO)

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...

Register for WiDS Livermore on March 13

The annual Women in Data Science (WiDS) conference returns on Wednesday, March 13. This is the seventh year for WiDS Livermore, which is independently organized by LLNL to be part of the mission to increase participation of women in data science and to feature outstanding women doing outstanding work. The all-day WiDS Livermore event is free and will be presented in a hybrid format. Everyone...

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...

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...

Young leaders learn from Nobel Laureates at Science and Technology in Society Forum

Early-career staff scientists Kelli Humbird, Chris Young and Brian Giera (director of LLNL's Data Science Institute) connected with Nobel Laureates and discussed important global issues ranging from AI to climate change at the 20th annual meeting of the Science and Technology in Society (STS) Forum in Kyoto, Japan. Lab Director Kim Budil, Acting Chief of Staff Ashley Bahney and Strategic...

LLNL, University of California partner for AI-driven additive manufacturing research

Grace Gu, a faculty member in mechanical engineering at UC Berkeley, has been selected as the inaugural recipient of the LLNL Early Career UC Faculty Initiative. The initiative is a joint endeavor between LLNL’s Strategic Deterrence Principal Directorate and UC national laboratories at the University of California Office of the President, seeking to foster long-term academic partnerships and...

Explainable artificial intelligence can enhance scientific workflows

As ML and AI tools become more widespread, a team of researchers in LLNL’s Computing and Physical and Life Sciences directorates are trying to provide a reasonable starting place for scientists who want to apply ML/AI, but don’t have the appropriate background. The team’s work grew out of a Laboratory Directed Research and Development project on feedstock materials optimization, which led to...