Newsletter

Lawrence Livermore National Laboratory (LLNL) participated in the first 1,000 Scientist AI Jam Session, bringing together over 1,400 DOE scientists to explore AI's role in scientific discovery. Hosted at nine national labs, the event allowed hands-on testing of advanced AI models for research applications. 

 Brian Spears, director of LLNL’s AI Innovation Incubator, emphasized, "We want to get these models in the hands of staff… and help them understand what they can do with them and what they can't yet do with them." Over 300 LLNL experts worked in teams to test AI’s capabilities in data...

OpenAI and the U.S. Department of Energy (DOE) teamed up to push the boundaries of AI-driven science, with over 1,000 DOE scientists engaged as part of this effort. On February 28, the 1,000 Scientist AI Jam brought together researchers from across the DOE complex—including over 300 at Lawrence Livermore National Laboratory (LLNL)—to test OpenAI’s cutting-edge o3 model. 

This event was a chance for scientists to tackle challenging problems with a state-of-the-art AI tool, share expertise, and explore how AI can transform research. Attending solo and as part of teams, participants engaged...

Launched in December 2024, “Safety in Artificial Intelligence: Challenges and Opportunities for the U.S. National Labs and Beyond” is a collaborative report resulting from last year’s “Strategy Alignment on AI Safety” workshop convened by LLNL and University of California (UC) at the UC Livermore Collaboration Center. The report underscores a need for safeguarded AI technologies, making recommendations for the role that national labs and others can play in developing novel evaluation methodologies that allow full considerations of risks/threats of AI technologies in different domains. The...

The DSI’s expansion this year to add several new programs—and therefore new ambassadors representing myriad areas of the Lab—has opened the door to collaboration opportunities. On October 22, the ambassadors convened in a first-time DSI Summit to share their 2025 plans and discuss ways to benefit the data science community. These DSI programs are the Consulting Service, student internships (the Data Science Summer Institute and Data Science Challenge), scientific outreach (including workshops and seminars), staff training, open data science (including the Open Data Initiative), and Women in...

LLNL’s Office of the Deputy Director for Science and Technology and the DSI co-sponsored a colloquium on October 3 featuring Stuart Russell, a distinguished professor at UC Berkeley. Russell, known for his pioneering work in artificial intelligence (AI), presented a compelling vision of the future of AI safety. He discussed AI safety principles, emphasizing human control and the need to plan for AI systems that may surpass human capabilities. He introduced the "loophole principle," indicating that efforts to correct misaligned AI might be futile. He referenced aviation and nuclear power as...

On Thursday, October 3, Dr. Stuart Russell will present a colloquium on general AI safety at LLNL. Co-sponsored by the Office of the Deputy Director for Science and Technology and the DSI, the seminar will begin at 10:05am Pacific, and external audiences are welcome to join remotely. Contact DSI-Seminars [at] llnl.gov (DSI-Seminars[at]llnl[dot]gov) to request a WebEx link.

Abstract: The media are agog with claims that recent advances in AI put artificial general intelligence (AGI) within reach. Is this true? If so, is that a good thing? Alan Turing predicted that AGI would result in the machines taking control. I will argue...

On July 16, the Department of Energy (DOE) 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, the United States stands to lose its competitive scientific edge and ability to maintain our national and economic security, will have a less diverse and competitive innovation AI ecosystem, will...

Former DSI director Michael Goldman and DSI Council member Barry Chen were recently named as Distinguished Members of Technical Staff (DMTS) for their extraordinary scientific and technical contributions to the Lab. DMTS is the highest technical staff level achievable by a Livermore scientist or engineer.

Goldman founded the DSI in 2018 and was its first director until 2023, establishing a strong, forward-leaning vision and consistent funding stream. He has been a technical lead on several Global Security projects and programs in computer vision, imagery, machine learning (ML), large language...

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 Department of Energy (DOE) exhibition booth. Notable attendees included DOE Deputy Secretary  David Turk, DOE Under Secretary  for Science and Innovation Geraldine Richmond, NNSA administrator Jill Hruby, DOE director of the Office of Critical and Emerging Technologies Helena Fu, US Senate majority leader Chuck Schumer, and White House Office of Science and Technology...

This recent LLNL news article by Jeremy Thomas is reprinted here in full. A direct, shareable link is here. All photos were taken by Randy Wong/LLNL.

Representatives from the Department of Energy (DOE) national laboratories, academia and industry convened recently at the University of California Livermore Collaboration Center (UCLCC) for a workshop aimed at aligning strategies for ensuring safe artificial intelligence (AI).

The daylong event, attended by dozens of AI researchers, included keynote speeches by thought leaders, panels by technical researchers and policymakers and breakout...

When 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 AI/ML research requires corresponding growth in the DSI’s efforts. “Our field is booming,” Giera states. “The Lab has a stake in the larger AI/ML community, especially when it comes to national security, and is investing accordingly in research and supporting programs. The future is wide open.”

For the DSI, a brighter spotlight on data science and AI/ML translates into staff opportunities. Gonzales points...

Zeroth-order (ZO) optimization algorithms enable the training of non-differentiable machine learning (ML) pipelines by iteratively estimating the gradient (i.e., the slope of a function) and updating the solution accordingly. This type of optimization has proved useful for relatively small-scale ML problems, but effectively scaling ZO algorithms to train deep neural networks can come at the expense of solution accuracy.

Developed by LLNL researchers James Diffenderfer, Bhavya Kailkhura, and Konstantinos Parasyris along with colleagues from Michigan State University and the University of...

You can still sign up for the Lab’s Women in Data Science (WiDS) conference taking place on Wednesday, March 13. This hybrid event is free and open to everyone—inside or outside the Lab, any career level, and data science experience level. The registration link and other details are posted at data-science.llnl.gov/wids.

• Register by March 1
• Hosted at the University of California Livermore Collaboration Center (UCLCC) and virtually
• Sponsored by LLNL’s Data Science Institute; Computing Principal Directorate; and Office of Inclusion, Diversity, Equity, and Accountability
• This regional...

Happy new year from the DSI! Although a new calendar year has begun, LLNL is already a quarter of the way into Fiscal Year 2024. Each FY brings new goals, challenges, and opportunities to all areas of the Lab, including the DSI. With increased emphasis and prioritization of data science, particularly artificial intelligence (AI) and machine learning (ML), at the national level, the U.S. Department of Energy and LLNL recognize the importance of investing in projects and programs that harness these technologies safely and securely.

Our community has grown inside and outside the Lab—this...

On October 30, the White House announced a new Executive Order (EO) that “establishes new standards for AI [artificial intelligence] safety and security, protects Americans’ privacy, advances equity and civil rights, stands up for consumers and workers, promotes innovation and competition, advances American leadership around the world, and more.”

The Department of Energy (DOE), with input from national labs, is in the process of responding to the EO on multiple fronts. Representatives from the DSI and AI Innovation Incubator (AI3) are working closely with LLNL leadership to provide the DOE...

In September, the DSI leadership team gathered for a two-day retreat to discuss goals, strategies, and activities for the next several years. The team consists of the director, deputy director (see next story), the council, directors of both student programs, data science ambassadors, and administrative and communication leads. As the hub of the Lab’s data science community, the DSI must evolve as LLNL’s mission space and the field evolve.

Some of the DSI’s plans for the new fiscal year, which began on October 1, include refining the Data Science Summer Institute and Data Science Challenge...

For the first time, students from the University of California’s Merced and Riverside campuses joined forces for the two-week Data Science Challenge at LLNL, tackling a real-world problem in machine learning (ML)-assisted heart modeling. Held during July 10–21, the event brought together 35 UC 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. According to organizers, the purpose of the challenge was to give students a taste of the broad scope of work...

The DSI’s Open Data Initiative (ODI) recently added a new project to the catalog: Computational Fluid Dynamics Simulation Data of Spatial Deposition. In fluid mechanics problems, computational fluid dynamics (CFD) uses data structures and numerical analysis to investigate the flow of liquids and gases. For instance, CFD models can simulate atmospheric transport and dispersion, as in this dataset’s simulations of wind-driven pollutant dispersion and deposition. The data is then used to train machine learning models that, in turn, can predict spatial patterns with high accuracy.

This new...

Since the DSI’s founding in 2018, the Lab has seen tremendous growth in its data science community and has invested heavily in related research. Five years later, the DSI has found its stride with a multipronged strategy of raising awareness about the field, encouraging partnerships across the community, supporting researchers, and nurturing the next generation of data scientists. A new article chronicles the DSI’s successes and influence both at the Lab and in the external data science community.

Michael Goldman, who directed the DSI until this April, says, “The accomplishments of DSI are...