Data Science in the News

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Data Days brings DOE labs together for discussions on data management and more

Nov. 9, 2023 - 
Data researchers, developers, data managers, and program managers from the DOE national laboratories visited LLNL on October 24–26 to discuss the latest in data management, sharing, and accessibility at the 2023 DOE Data Days (D3) workshop. Sponsored by the National Nuclear Security Administration’s (NNSA) Office of Defense Nuclear Nonproliferation and hosted annually by LLNL, the event...

LLNL’s Kailkhura elevated to IEEE senior member

Nov. 8, 2023 - 
IEEE, the world’s largest technical professional organization, has elevated LLNL research staff member Bhavya Kailkhura to the grade of senior member within the organization. IEEE has more than 427,000 members in more than 190 countries, including engineers, scientists and allied professionals in the electrical and computer sciences, engineering and related disciplines. Just 10% of IEEE’s...

Data Science Summer Institute hosts student interns from Japan

Oct. 13, 2023 - 
The Data Science Summer Institute (DSSI) hosted summer student interns from Japan on-site for the first time, where the students worked with Lab mentors on real-world projects in AI-assisted bio-surveillance and automated 3D printing. From June to September, the three students—Raiki Yoshimura, Shinnosuke Sawano and Taisei Saida—lived in rental apartments near the Lab and worked at the Lab on...

Explainable artificial intelligence can enhance scientific workflows

July 25, 2023 - 
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...

Celebrating the DSI’s first five years

May 18, 2023 - 
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...

Cognitive simulation supercharges scientific research

Jan. 10, 2023 - 
Computer modeling has been essential to scientific research for more than half a century—since the advent of computers sufficiently powerful to handle modeling’s computational load. Models simulate natural phenomena to aid scientists in understanding their underlying principles. Yet, while the most complex models running on supercomputers may contain millions of lines of code and generate...

LLNL researchers win HPCwire award for applying cognitive simulation to ICF

Nov. 17, 2022 - 
The high performance computing publication HPCwire announced LLNL as the winner of its Editor’s Choice award for Best Use of HPC in Energy for applying cognitive simulation (CogSim) methods to inertial confinement fusion (ICF) research. The award was presented at the largest supercomputing conference in the world: the 2022 International Conference for High Performance Computing, Networking...

ESGF launches effort to upgrade climate projection data system

Oct. 5, 2022 - 
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...

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

Sept. 21, 2022 - 
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...

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

Aug. 2, 2022 - 
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...

Assured and robust…or bust

June 30, 2022 - 
The consequences of a machine learning (ML) error that presents irrelevant advertisements to a group of social media users may seem relatively minor. However, this opacity, combined with the fact that ML systems are nascent and imperfect, makes trusting their accuracy difficult in mission-critical situations, such as recognizing life-or-death risks to military personnel or advancing materials...

CASC team wins best paper at visualization symposium

May 25, 2022 - 
A research team from LLNL’s Center for Applied Scientific Computing won Best Paper at the 15th IEEE Pacific Visualization Symposium (PacificVis), which was held virtually on April 11–14. Computer scientists Harsh Bhatia, Peer-Timo Bremer, and Peter Lindstrom collaborated with University of Utah colleagues Duong Hoang, Nate Morrical, and Valerio Pascucci on “AMM: Adaptive Multilinear Meshes.”...

Unprecedented multiscale model of protein behavior linked to cancer-causing mutations

Jan. 10, 2022 - 
LLNL researchers and a multi-institutional team have developed a highly detailed, machine learning–backed multiscale model revealing the importance of lipids to the signaling dynamics of RAS, a family of proteins whose mutations are linked to numerous cancers. Published by the Proceedings of the National Academy of Sciences, the paper details the methodology behind the Multiscale Machine...

LLNL establishes AI Innovation Incubator to advance artificial intelligence for applied science

Dec. 20, 2021 - 
LLNL has established the AI Innovation Incubator (AI3), a collaborative hub aimed at uniting experts in artificial intelligence (AI) from LLNL, industry and academia to advance AI for large-scale scientific and commercial applications. LLNL has entered into a new memoranda of understanding with Google, IBM and NVIDIA, with plans to use the incubator to facilitate discussions and form future...

Building confidence in materials modeling using statistics

Oct. 31, 2021 - 
LLNL statisticians, computational modelers, and materials scientists have been developing a statistical framework for researchers to better assess the relationship between model uncertainties and experimental data. The Livermore-developed statistical framework is intended to assess sources of uncertainty in strength model input, recommend new experiments to reduce those sources of uncertainty...

Inaugural industry forum inspires ML community

Sept. 16, 2021 - 
LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12. Co-hosted by the Lab’s High-Performance Computing Innovation Center (HPCIC) and Data Science Institute (DSI), the virtual event brought together more than 500 enrollees from the Department of Energy (DOE) complex, commercial companies, professional societies, and academia. Industry sponsors included...

Former interns share insights during career panel

Aug. 19, 2021 - 
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...

Conference papers highlight importance of data security to machine learning

May 12, 2021 - 
The 2021 Conference on Computer Vision and Pattern Recognition, the premier conference of its kind, will feature two papers co-authored by an LLNL researcher targeted at improving the understanding of robust machine learning models. Both papers include contributions from LLNL computer scientist Bhavya Kailkhura and examine the importance of data in building models, part of a Lab effort to...

Winter hackathon highlights data science talks and tutorial

March 24, 2021 - 
The Data Science Institute (DSI) sponsored LLNL’s 27th hackathon on February 11–12. Held four times a year, these seasonal events bring the computing community together for a 24-hour period where anything goes: Participants can focus on special projects, learn new programming languages, develop skills, dig into challenging tasks, and more. The winter hackathon was the DSI’s second such...

Lab researchers explore ‘learn-by-calibration’ approach to deep learning to accurately emulate scientific process

Feb. 10, 2021 - 
An LLNL team has developed a “Learn-by-Calibrating” method for creating powerful scientific emulators that could be used as proxies for far more computationally intensive simulators. Researchers found the approach results in high-quality predictive models that are closer to real-world data and better calibrated than previous state-of-the-art methods. The LbC approach is based on interval...