Data Science in the News

Virtual seminar series explores data-driven physical simulations

The rapidly growing fields of artificial intelligence (AI) and machine learning (ML) have become cornerstones of LLNL’s data science research activities. The Lab’s scientific community regularly publishes advancements in both AI/ML applications and theory, contributing to international discourse on the possibilities of these compelling technologies. The large volume of AI/ML scientific...

COVID-19 HPC Consortium reflects on past year

COVID-19 HPC Consortium scientists and stakeholders met virtually on March 23 to mark the consortium’s one-year anniversary, discussing the progress of research projects and the need to pursue a broader organization to mobilize supercomputing access for future crises. The White House announced the launch of the public-private consortium, which provides COVID-19 researchers with free access to...

Lab event encourages growth of women in data science

Coinciding with International Women’s Day on March 8, LLNL’s 4th Women in Data Science (WiDS) regional event brought women together to discuss successes, opportunities and challenges of being female in a mostly male field. The Lab’s first-ever virtual WiDS gathering attracted dozens of LLNL data scientists as well as some from outside the Lab, and featured speakers, a career panel and...

Ana Kupresanin featured in FOE alumni spotlight

LLNL's Ana Kupresanin, deputy director of the Center for Applied Scientific Computing and member of the Data Science Institute council, was recently featured in a Frontiers of Engineering (FOE) alumni spotlight. Kupresanin develops statistical and machine learning models that incorporate real-world variability and probabilistic behavior to quantify uncertainties in engineering and physics...

LLNL physicist wins Young Former Student award

Texas A&M University’s Department of Nuclear Engineering on December 10 announced it has honored LLNL physicist Kelli Humbird with its 2020-21 Young Former Student award for her work at LLNL in combining machine learning with inertial confinement fusion (ICF) research. Humbird graduated from Texas A&M with a PhD in nuclear engineering in 2019. Since joining the Laboratory as an intern in 2016...

DOE announces five new energy projects at LLNL

The DOE today announced two rounds of awards for the High Performance Computing for Energy Innovation Program HPC4EI), including five projects at LLNL. HPC4EI connects industry with the computational resources and expertise of the DOE national laboratories to solve challenges in manufacturing, accelerate discovery and adoption of new materials and improve energy efficiency. The awards were...

The internship that launched a machine-learning target revolution

Kelli Humbird came to LLNL as a student intern and became a teacher of new data science techniques. In this profile, she describes her experiences and the path that led to her research inertial confinement fusion. Read more at the National Ignition Facility.

Machine learning speeds up and enhances physics calculations

Interpreting data from NIF’s cutting-edge high energy density science experiments relies on physics calculations that are so complex they can challenge LLNL supercomputers, which stand among the best in the world. A collaboration between LLNL and French researchers found a novel way to incorporate machine learning and neural networks to significantly speed up inertial confinement fusion...

Modeling neuronal cultures on 'brain-on-a-chip' devices

For the past several years, LLNL scientists and engineers have made significant progress in development of a three-dimensional “brain-on-a-chip” device capable of recording neural activity of human brain cell cultures grown outside the body. The team has developed a statistical model for analyzing the structures of neuronal networks that form among brain cells seeded on in vitro brain-on-a...

Carnegie Live: high energy density science and AI (VIDEO)

In this Carnegie Live video, Seiichi Shimasaki, Science Counselor for the Japanese embassy in the U.S., described a multiyear science research program (nicknamed the “Moonshot”) to develop new technologies that help solve some of society’s most pressing challenges. He explained that the Government of Japan was looking for a data science program to mentor young scientists, which led to the...

The incorporation of machine learning into scientific simulations at LLNL (VIDEO)

In this video from the Stanford HPC Conference, Katie Lewis presents "The Incorporation of Machine Learning into Scientific Simulations at Lawrence Livermore National Laboratory." Read more and watch the video at insideHPC.

Lab promotes diversity, tech at Women in Data Science regional event

For the third consecutive year, Lawrence Livermore National Laboratory (LLNL) hosted a Women in Data Science (WiDS) regional event on March 2. Held at the HPC Innovation Center, the event drew dozens of attendees from LLNL, Sandia National Laboratories, local universities, and Bay Area commercial companies. Livermore was one of over 200 regional events in 60 countries coordinated with the...

Local Women in Data Science conference showcases Lab research

For the third consecutive year, LLNL hosted a Women in Data Science (WiDS) regional event on March 2. The event drew dozens of attendees from LLNL, Sandia National Laboratories, local universities, and Bay Area commercial companies. Livermore was one of over 200 regional events in 60 countries coordinated with the main WiDS conference at Stanford University. According to the WiDS website...

Deep learning may provide solution for efficient charging, driving of autonomous electric vehicles

LLNL computer scientists and software engineers have developed a deep learning-based strategy to maximize electric vehicle (EV) ride-sharing services while reducing carbon emissions and the impact to the electrical grid, emphasizing autonomous EVs capable of offering 24-hour service. Read more at LLNL News.

Can machine learning improve computer models enough to ignite internal confinement fusion?

Laser-fusion researchers have turned to machine-learning techniques to seek the combinations of laser pulse characteristics and target design needed to optimize target implosions for inertial confinement fusion. Machine learning has tripled the fusion yield of the simpler direct-drive approach being studied with the OMEGA laser at the University of Rochester. In principle, the Rochester...

Department of Energy researchers share data management strategies at first-ever “Data Day”

It’s become something of a mantra of the digital age: Data is the new currency. Especially in science, where it’s hard to find a single project that doesn’t involve generating or consuming massive amounts of data. In light of the growing awareness of the critical importance of data management across the Department of Energy complex, more than 100 researchers from DOE national laboratories...

Big data illuminates the physical sciences

Livermore teams are applying innovative data analysis and interpretation techniques to advance fundamental science research. This article describes projects in astrophysics and materials science. Read more at Science & Technology Review.

Collaboration drives data science workshop

Lawrence Livermore National Laboratory’s (LLNL’s) Data Science Institute (DSI) hosted its second annual workshop on July 23–24, 2019. Co-sponsored by the University of California (UC) system, the event drew more than 200 participants to Garré Winery in Livermore. A common theme ran throughout both days: Collaboration is always welcome. Indeed, feedback from last year’s workshop inspired a...

CANDLE illuminates new pathways in fight against cancer

As part of the Department of Energy’s role in the fight against cancer, scientists are building tools that use supercomputers to solve problems in entirely new ways. Cancer research provides a complex deep learning challenge that enables DOE to develop new supercomputing capabilities that will, in turn, help scientists address challenges in national security and science. The CANcer...

Hyperion Research announces new winners of HPC Innovation Excellence Awards

Hyperion Research  announced the 14th round of recipients of the HPC Innovation Excellence Award at the ISC19 supercomputer industry conference in Frankfurt, Germany. Led by Brian Spears, an LLNL team used the Trinity supercomputer to seek out successful modes of laser-driven fusion implosions by building an enormous database for supervised training of a machine learned surrogate...