Data Science in the News

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...

Defending U.S. critical infrastructure from nation-state cyberattacks

July 21, 2022- 
For many years, LLNL has been conducting research on cybersecurity, as well as defending its systems and networks from cyberattacks. The Lab has developed an array of capabilities to detect and defend against cyberintruders targeting IT networks and worked with government agencies and private-sector partners to share its cybersecurity knowledge to the wider cyberdefense community. LLNL has...

Introduction to deep learning for image classification workshop (VIDEO)

July 6, 2022- 
In addition to its annual conference held every March, the global Women in Data Science (WiDS) organization hosts workshops and other activities year-round to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field. On June 29, LLNL’s Cindy Gonzales led a WiDS Workshop titled “Introduction to Deep Learning for Image Classification.” The abstract...

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.”...

Paving the way to tailor-made carbon nanomaterials and more accurate energetic materials modeling

March 17, 2022- 
To better understand how carbon nanomaterials could be tailor-made and how their formation impacts shock phenomena such as detonation, LLNL scientists conducted machine-learning-driven atomistic simulations to provide insight into the fundamental processes controlling the formation of nanocarbon materials, which could serve as a design tool, help guide experimental efforts and enable more...

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...

Understanding materials behavior with data science (VIDEO)

Dec. 21, 2021- 
Computational chemist Rebecca Lindsey, PhD, explains how machine learning and data science techniques are used to develop diagnostic tools for stockpile stewardship, such as models that predict detonator performance. Lindsey also describes how atomistic simulations improve researchers’ understanding of the microscopic phenomena that govern the chemistry in materials under extreme conditions...

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 better materials with data science (VIDEO)

Nov. 11, 2021- 
Research engineer Brian Giera, PhD, describes how data science techniques help collect and analyze data from advanced manufacturing processes in order to craft meaningful experiments. With examples of automated microencapsulation, 3D nanoprinting, metal additive manufacturing, laser track welding, and digital twins, Giera explains how interdisciplinary teams apply machine learning to remove...

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...

Data Science Challenge welcomes UC Riverside

Oct. 11, 2021- 
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...

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...

LLNL, NNSA and elected officials celebrate opening of Livermore Valley Open Campus expansion

Aug. 26, 2021- 
Leaders from the NNSA, Congressional representatives and local elected officials gathered at LLNL on August 10 to celebrate an expansion to the Livermore Valley Open Campus (LVOC). The Lab hosted a ribbon-cutting ceremony for a new office building (Bldg. 642) and a conference annex (Bldg. 643), which will provide modern office and meeting space for LLNL researchers in predictive biology...

Machine learning aids in materials design

June 10, 2021- 
A long-held goal by chemists across many industries is to imagine the chemical structure of a new molecule and be able to predict how it will function for a desired application. In practice, this vision is difficult, often requiring extensive laboratory work to synthesize, isolate, purify, and characterize newly designed molecules to obtain the desired information. Recently, a team of LLNL...

Laser-driven ion acceleration with deep learning

May 25, 2021- 
While advances in machine learning over the past decade have made significant impacts in applications such as image classification, natural language processing and pattern recognition, scientific endeavors have only just begun to leverage this technology. This is most notable in processing large quantities of data from experiments. Research conducted at LLNL is the first to apply neural...

The data-driven future of extreme physics

May 19, 2021- 
By applying modern machine learning and data science methods to “extreme” plasma physics, researchers can gain insight into our universe and find clues about creating a limitless amount of energy. In a recent perspective published in Nature, LLNL scientists and international collaborators outline key challenges and future directions in using machine learning and other data-driven techniques...

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...

A winning strategy for deep neural networks

April 29, 2021- 
LLNL continues to make an impact at top machine learning conferences, even as much of the research staff works remotely during the COVID-19 pandemic. Postdoctoral researcher James Diffenderfer and computer scientist Bhavya Kailkhura, both from LLNL’s Center for Applied Scientific Computing, are co-authors on a paper—“Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural...

Lab offers forum on machine learning for industry

April 22, 2021- 
LLNL is looking for participants and attendees from industry, research institutions and academia for the first-ever Machine Learning for Industry Forum (ML4I), a three-day virtual event starting Aug. 10. The event is sponsored by LLNL’s High Performance Computing Innovation Center and the Data Science Institute. The deadline for submitting presentations or industry use cases is June 30. The...