Data Science in the News

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

Nov. 9, 2021 - 
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. Alo...

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...

LLNL joins Human Vaccines Project to accelerate vaccine development and understanding of immune response

Oct. 21, 2021 - 
LLNL has joined the international Human Vaccines Project (HVP), bringing Lab expertise and computing resources to the consortium to aid development of a universal coronavirus vaccine and improve understanding of immune response. The HVP is a nonprofit, public-private partnership with a mission to decode the human immune system and accelerate the development of vaccines and immunotherapies...

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...

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...

60 years of cancer research

Sept. 10, 2021 - 
From studying radioactive isotope effects to better understanding cancer metastasis, the Laboratory’s relationship with cancer research endures some 60 years after it began, with historical precedent underpinning exciting new research areas. In one Cancer Moonshot project, research includes a close synergy between experiments and computation, allowing scientists to get a better picture of the...

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...

New machine-learning tactic sharpens NIF shot predictions

July 8, 2021 - 
Inertial confinement fusion (ICF) experiments at LLNL's National Ignition Facility (NIF) are extremely complex and costly, and it is challenging to accurately and consistently predict the outcome. But that is now changing, thanks to the work of design physicists. In a paper recently published in Physics of Plasmas, design physicist Kelli Humbird and her colleagues describe a new machine...

Career panel series kicks off with women in Computing leadership roles

July 6, 2021 - 
More than 100 LLNL staff and students gathered virtually for the first session of a new career panel series inspired by the annual WiDS conference and sponsored by the DSI. Panelists discussed how they have shaped their careers at the Lab and in Computing, their journeys into leadership roles, and how they navigate career challenges. Data scientist and panel series organizer Cindy Gonzales...

Virtual LLNL-UC Merced Data Science Challenge tackles asteroid detection though machine learning

June 25, 2021 - 
Over three weeks, students from the University of California, Merced collaborated online with mentors at LLNL to tackle a real-world challenge problem: using machine learning to identify potentially hazardous asteroids that could pose an existential threat to humanity. Throughout the event, the teams tackled problems around the theme of “Astronomy for Planetary Defense.” For the main...

Brian Gallagher combines science with service

June 20, 2021 - 
Brian Gallagher works on applications of machine learning for a variety of science and national security questions. He’s also a group leader, student mentor, and the new director of LLNL’s Data Science Challenge. The Lab has enabled Gallagher to combine scientific pursuits with leadership positions and people-focused responsibilities. “For a long time, my primary motivation was learning new...

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...

COVID-19 detection and analysis with Nisha Mulakken (VIDEO)

June 7, 2021 - 
LLNL biostatistician Nisha Mulakken has enhanced the Lawrence Livermore Microbial Detection Array (LLMDA) system with detection capability for all variants of SARS-CoV-2. The technology detects a broad range of organisms—viruses, bacteria, archaea, protozoa, and fungi—and has demonstrated novel species identification for human health, animal health, biodefense, and environmental sampling...

Lab postdocs invited to prestigious Heidelberg Forum

May 21, 2021 - 
Two LLNL postdoctoral researchers are among a select group of 200 scientists invited to attend the 8th Heidelberg Laureate Forum, an international conference that connects young researchers with laureates of the major prizes in mathematics and computer science. For a week in September, LLNL computational engineering postdocs Ruben Glatt and Felipe Leno da Silva will meet and interact with...

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...

DSI virtual seminar series debuts on YouTube

May 19, 2021 - 
Since launching in 2018, the DSI has hosted more than three dozen speakers in its seminar series. These events invite researchers from academia, industry, and other institutions to discuss their work for an hour to an LLNL audience. In 2020, the series transitioned to a virtual format, and a video playlist of recently recorded seminars is available on the Livermore Lab Events YouTube channel...

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...

Advanced Data Analytics for Proliferation Detection shares technical advances during two-day meeting

May 7, 2021 - 
The Advanced Data Analytics for Proliferation Detection (ADAPD) program held a two-day virtual technical exchange meeting recently. The goal of the meeting was to highlight the science-based and data-driven analysis work conducted by ADAPD to advance the state-of-the-art to accelerate artificial intelligence (AI) innovation and develop AI-enabled systems to enhance the United States’...

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...