Data Science in the News

UC Merced & UC Riverside tackle Data Science Challenge on ML-assisted heart modeling

For the first time, students from the University of California (UC) Merced and UC Riverside joined forces for the two-week Data Science Challenge (DSC) at LLNL, tackling a real-world problem in machine learning (ML)-assisted heart modeling. Held in the Livermore Valley Open Campus’s newly remodeled University of California Livermore Collaboration Center from July 10-21, the event brought...

High-performance computing, AI and cognitive simulation helped LLNL conquer fusion ignition

For hundreds of LLNL scientists on the design, experimental, and modeling and simulation teams behind inertial confinement fusion (ICF) experiments at the National Ignition Facility, the results of the now-famous Dec. 5, 2022, ignition shot didn’t come as a complete surprise. The “crystal ball” that gave them increased pre-shot confidence in a breakthrough involved a combination of detailed...

Celebrating the DSI’s first five years

View the LLNL Flickr album Data Science Institute Turns Five. Data science—a field combining technical disciplines such as computer science, statistics, mathematics, software development, domain science, and more—has become a crucial part of how LLNL carries out its mission. Since the DSI’s founding in 2018, the Lab has seen tremendous growth in its data science community and has invested...

Patent applies machine learning to industrial control systems

An industrial control system (ICS) is an automated network of devices that make up a complex industrial process. For example, a large-scale electrical grid may contain thousands of instruments, sensors, and controls that transfer and distribute power, along with computing systems that capture data transmitted across these devices. Monitoring the ICS network for new device connections, device...

Computing codes, simulations helped make ignition possible

Harkening back to the genesis of LLNL’s inertial confinement fusion (ICF) program, codes have played an essential role in simulating the complex physical processes that take place in an ICF target and the facets of each experiment that must be nearly perfect. Many of these processes are too complicated, expensive, or even impossible to predict through experiments alone. With only a few...

Supercomputing’s critical role in the fusion ignition breakthrough

On December 5th, the research team at LLNL's National Ignition Facility (NIF) achieved a historic win in energy science: for the first time ever, more energy was produced by an artificial fusion reaction than was consumed—3.15 megajoules produced versus 2.05 megajoules in laser energy to cause the reaction. High-performance computing was key to this breakthrough (called ignition), and HPCwire...

National Ignition Facility achieves fusion ignition

The U.S. Department of Energy (DOE) and DOE’s National Nuclear Security Administration (NNSA) today announced the achievement of fusion ignition at LLNL—a major scientific breakthrough decades in the making that will pave the way for advancements in national defense and the future of clean power. On Dec. 5, a team at LLNL’s National Ignition Facility (NIF) conducted the first controlled...

Understanding the universe with applied statistics (VIDEO)

In a new video posted to the Lab’s YouTube channel, statistician Amanda Muyskens describes MuyGPs, her team’s innovative and computationally efficient Gaussian Process hyperparameter estimation method for large data. The method has been applied to space-based image classification and released for open-source use in the Python package MuyGPyS. MuyGPs will help astronomers and astrophysicists...

ESGF launches effort to upgrade climate projection data system

The Earth System Grid Federation (ESGF), a multi-agency initiative that gathers and distributes data for top-tier projections of the Earth’s climate, is preparing a series of upgrades that will make using the data easier and faster while improving how the information is curated. The federation, led by the Department of Energy’s Oak Ridge National Laboratory in collaboration with Argonne and...

Scientific discovery for stockpile stewardship

Among the significant scientific discoveries that have helped ensure the reliability of the nation’s nuclear stockpile is the advancement of cognitive simulation. In cognitive simulation, researchers are developing AI/ML algorithms and software to retrain part of this model on the experimental data itself. The result is a model that “knows the best of both worlds,” says Brian Spears, a...

LLNL to cooperate with University of Utah's one oneAPI Center of Excellence

The University of Utah has announced the creation of a new oneAPI Center of Excellence focused on developing portable, scalable and performant data compression techniques. The oneAPI Center will be headed out of the University of Utah’s Center for Extreme Data Management Analysis and Visualization (CEDMAV) and will involve the cooperation of LLNL’s Center for Applied Scientific Computing. It...

S&TR cover story: The ACES in our hand

Uranium enrichment is central to providing fuel to nuclear reactors, even those intended only for power generation. With minor modifications, however, this process can be altered to yield highly enriched uranium for use in nuclear weapons. The world’s need for nuclear fuel coexists with an ever-present danger—that a nonnuclear weapons nation-state possessing enrichment technology could...

Lab researchers win top award for machine learning-based approach to ICF experiments

The IEEE Nuclear and Plasma Sciences Society (NPSS) announced an LLNL team as the winner of its 2022 Transactions on Plasma Science Best Paper Award for their work applying machine learning to inertial confinement fusion (ICF) experiments. In the paper, lead author Kelli Humbird and co-authors propose a novel technique for calibrating ICF experiments by combining machine learning with...

An open-source, data-science toolkit for energy: GridDS

As the number of smart meters and the demand for energy is expected to increase by 50% by 2050, so will the amount of data those smart meters produce. While energy standards have enabled large-scale data collection and storage, maximizing this data to mitigate costs and consumer demand has been an ongoing focus of energy research. An LLNL team has developed GridDS—an open-source, data-science...

Winter hackathon meets WiDS datathon

Sponsored by the DSI, LLNL’s winter hackathon took place on February 16–17. Hackathons are 24-hour events that encourage collaborative programming and creative problem solving. In addition to traditional hacking, the hackathon included a special datathon competition in anticipation of the Women in Data Science (WiDS) conference on March 7. Hackathon and datathon participants presented their...

LLNL-led team uses machine learning to derive black hole motion from gravitational waves

To understand the motion of binary black holes, researchers have traditionally simplified Einstein’s field equations and solved them to calculate the emitted gravitational waves. The approach is complex and requires expensive, time-consuming simulations on supercomputers or approximation techniques that can lead to errors or break down when applied to more complicated black hole systems. A...

Summer scholar develops data-driven approaches to key NIF diagnostics

Su-Ann Chong's summer project, “A Data-Driven Approach Towards NIF Neutron Time-of-Flight Diagnostics Using Machine Learning and Bayesian Inference,” is aimed at presenting a different take on nToF diagnostics. Neutron time-of-flight diagnostics are an essential tool to diagnose the implosion dynamics of inertial confinement fusion experiments at NIF, the world’s largest and most energetic...

Data Science Challenge welcomes UC Riverside

Together with LLNL’s Center for Applied Scientific Computing (CASC), the DSI welcomed a new academic partner to the 2021 Data Science Challenge (DSC) internship program: the University of California (UC) Riverside campus. The intensive program has run for three years with UC Merced, and it tasks undergraduate and graduate students with addressing a real-world scientific problem using data...

Tackling the COVID-19 pandemic

To help the U.S. fight the COVID-19 pandemic, LLNL did what it does best: quickly bring together interdisciplinary teams and diverse technologies to address urgent national challenges. This effort includes applying advanced high-performance computing resources to biological research and anayzing complicated computer models and enormous datasets. Read more in Science & Technology Review.

Former interns share insights during career panel

The DSI’s new career panel series continued on August 10 with a session featuring former LLNL interns who converted to full-time employment at the Lab. Inspired by the annual Women in Data Science conference, the panel session was open to all LLNL staff and students. Moderator Mary Silva was joined by panelists from the Computing and Engineering Directorates: Brian Bartoldson, Jose Cadena...