Data Science in the News

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, DOD, NNSA dedicate Rapid Response Laboratory and supercomputing system to accelerate biodefense

LLNL recently welcomed officials from the Department of Defense (DOD) and National Nuclear Security Administration (NNSA) to dedicate a new supercomputing system and Rapid Response Laboratory (RRL). DOD is working with NNSA to significantly increase the computing capability available to the national biodefense programs. The collaboration has enabled expanding systems of the same architecture...

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...

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...

Probing carbon capture, atom-by-atom

A team of scientists at LLNL has developed a machine-learning model to gain an atomic-level understanding of CO2 capture in amine-based sorbents. This innovative approach promises to enhance the efficiency of direct air capture (DAC) technologies, which are crucial for reducing the excessive amounts of CO2 already present in the atmosphere. The low cost of these sorbents has enabled several...

ISCP projects make machine learning advantages tangible

Data science tools are not only rapidly taking hold across disciplines, they are constantly evolving. The applications, services, and techniques one cohort of scientists and engineers may have learned could be out of date by the next cohort, especially as machine learning (ML) and artificial intelligence (AI) tools become commonplace. To keep employees abreast of the latest tools, two data...

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...

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...

The Laboratory’s habit of innovation

LLNL’s HPC and data science 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. The latest issue of Science & Technology Review features several award-winning projects, including ZFP and CANDLE: (1) ZFP introduces a new method of compressing large data sets...

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...

Statistical framework synchronizes medical study data

The risks and benefits of heart surgery, chemotherapy, vaccination, and other medical treatments can change based on the time of day they are administered. These variations arise in part due to changes in gene expression levels throughout the 24-hour day-night cycle, with around 50% of genes displaying oscillatory behavior. To evaluate new therapies, investigators study how a gene’s...

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...

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...

Machine learning tool fills in the blanks for satellite light curves

When viewed from Earth, objects in space are seen at a specific brightness, called apparent magnitude. Over time, ground-based telescopes can track a specific object’s change in brightness. This time-dependent magnitude variation is known as an object’s light curve, and can allow astronomers to infer the object’s size, shape, material, location, and more. Monitoring the light curve of...

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...