Data Science in the News

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ML model instantly predicts polymer properties

Nov. 30, 2022 - 
Hundreds of millions of tons of polymer materials are produced globally for use in a vast and ever-growing application space with new material demands such as green chemistry polymers, consumer packaging, adhesives, automotive components, fabrics and solar cells. But discovering suitable polymer materials for use in these applications lies in accurately predicting the properties that a...

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...

Understanding the universe with applied statistics (VIDEO)

Nov. 17, 2022 - 
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...

Papers win Test of Time awards at 2022 IEEE VIS conference

Oct. 31, 2022 - 
Two LLNL-led teams received SciVis Test of Time awards at the 2022 IEEE VIS conference on Oct. 18, for papers that have achieved lasting relevancy in the field of scientific visualization. Published in 2008, an LLNL-led paper that—for the first time—allowed Digital Morse Theory to be applied to large scale and three-dimensional data, won the 14-year Test of Time award for making a lasting...

Project co-led at LLNL looks to improve visualization of largescale datasets

Oct. 27, 2022 - 
LLNL researchers are starting work on a three-year project aimed at improving methods for visual analysis of large heterogeneous data sets as part of a recent Department of Energy funding opportunity. The joint project, entitled “Neural Field Processing for Visual Analysis,” will be led at LLNL by co-principal investigator (PI) Andrew Gillette. Gillette is joined by lead PI Matthew Berger at...

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...

LLNL team claims top AI award at international symbolic regression competition

Aug. 16, 2022 - 
An LLNL team claimed a top prize at an inaugural international symbolic regression competition for an artificial intelligence (AI) framework they developed capable of explaining and interpreting real-life COVID-19 data. Hosted by the open source SRBench project at the 2022 Genetic and Evolutionary Computation Conference (GECCO), the competition had two tracks—synthetic and real-world—and...

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...

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.”...

NNSA and Cornelis Networks to collaborate on next-generation high-performance networking

May 4, 2022 - 
The Next-Generation High Performance Computing Network (NG-HPCN) project for the NNSA’s Advanced Simulation and Computing (ASC) program will enable NNSA to co-design and partner with Cornelis on development and productization of next-generation interconnect technologies for HPC. The project is led by LLNL for the NNSA Tri-Labs: LLNL, Los Alamos and Sandia national laboratories. The resulting...

Accelerating the path to precision medicine

March 22, 2022 - 
LLNL joined the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) consortium in 2018. The national, multiyear, multidisciplinary effort, led by the University of California at San Francisco in collaboration with Lawrence Berkeley and Argonne national laboratories and other leading research organizations and universities, combines neuroimaging, blood-based...

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...

Winter hackathon meets WiDS datathon

March 9, 2022 - 
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...

COVID-19 R&D: Computing responds to pandemic

Jan. 19, 2022 - 
When the COVID-19 pandemic began, the Laboratory immediately started seeking solutions to the myriad challenges posed by the global crisis. The Computing Directorate jumped right in with research and development activities that combine molecular screening to inform antiviral drug experimentation; a generative molecular design software platform to optimize properties of antiviral drugs; an...

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...

Digital twins for cancer patients could be ‘paradigm shift’ for predictive oncology

Dec. 16, 2021 - 
A multi-institutional team, including an LLNL contributor, has proposed a framework for digital twin models of cancer patients that researchers say would create a “paradigm shift” for predictive oncology. Published online Nature Medicine on November 25, the proposed framework for Cancer Patient Digital Twins (CPDTs) — virtual representations of cancer patients using real-time data — would...

Career panel spotlights diversity, equity, and inclusion

Nov. 19, 2021 - 
The DSI’s career panel series continued on November 3 with a session highlighting diversity, equity, and inclusion (DEI) as well as the Lab’s DEI-focused employee resource groups (ERGs). ERGs are sponsored by LLNL’s Office of Strategic Diversity and Inclusion Programs. Moderator Anh Quach, member of the Asian Pacific American Council (APAC), was joined by four panelists: Raul Viera Mercado...

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...

Summer scholar develops data-driven approaches to key NIF diagnostics

Oct. 20, 2021 - 
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...