Data Science in the News

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...

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...

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...

Novel deep learning framework for symbolic regression

March 18, 2021- 
LLNL computer scientists have developed a new framework and an accompanying visualization tool that leverages deep reinforcement learning for symbolic regression problems, outperforming baseline methods on benchmark problems. The paper was recently accepted as an oral presentation at the International Conference on Learning Representations (ICLR 2021), one of the top machine learning...

CASC research in machine learning robustness debuts at AAAI conference

Feb. 10, 2021- 
LLNL’s Center for Applied Scientific Computing (CASC) has steadily grown its reputation in the artificial intelligence (AI)/machine learning (ML) community—a trend continued by three papers accepted at the 35th AAAI Conference on Artificial Intelligence, held virtually on February 2–9, 2021. Computer scientists Jayaraman Thiagarajan, Rushil Anirudh, Bhavya Kailkhura, and Peer-Timo Bremer led...

LLNL physicist wins Young Former Student award

Dec. 16, 2020- 
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...

NeurIPS papers aim to improve understanding and robustness of machine learning algorithms

Dec. 7, 2020- 
The 34th Conference on Neural Information Processing Systems (NeurIPS) is featuring two papers advancing the reliability of deep learning for mission-critical applications at LLNL. The most prestigious machine learning conference in the world, NeurIPS began virtually on Dec. 6. The first paper describes a framework for understanding the effect of properties of training data on the...

Machine learning speeds up and enhances physics calculations

Oct. 1, 2020- 
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...

AI identifies change in microstructure in aging materials

May 26, 2020- 
LLNL scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using AI. The work recently appeared online in the journal Computational Materials Science. Read more at LLNL News.

Livermore Lab Foundation awards scholarship to Cal State East Bay computer science student

Aug. 2, 2019- 
Alan Noun, a computer science student at Cal State University East Bay and the recipient of the Livermore Lab Foundation's first full-year scholarship, has started a summer internship at LLNL. In partnership with Cal State University East Bay (CSUEB), the Livermore Lab Foundation (LLF) awarded Noun a one-year stipend to allow him to devote more time to academics by reducing work he needs to...

CASC research showcased at major data science venues

March 20, 2019- 
Researchers from LLNL’s Center for Applied Scientific Computing (CASC) are among the Lab’s employees making waves in the data science community, with many prominent accolades, publications, and acceptances in 2018. Data science encompasses some of the hottest technology topics, and the Center’s computer scientists eagerly embrace the challenges of this rapidly growing domain. Read more at...

Going deep: Lab employees get an introduction to world of machine learning, neural networks

Feb. 1, 2019- 
Deep learning is one of the most popular and widely used machine learning methods due to its success with autonomous vehicles, speech recognition and image classification, to name a few emergent technologies. But what exactly is deep learning, and how can it best be applied to Lab projects? LLNL employees discovered the answers during a recent "Deep Learning 101" course, which introduced the...

LLNL explores machine learning to prevent defects in metal 3D-printed parts in real time

Sept. 13, 2018- 
LLNL researchers have developed machine learning algorithms capable of processing the data obtained during metal 3D printing in real time and detecting within milliseconds whether a 3D part will be of satisfactory quality. Read more at LLNL News.

Researchers developing deep learning system to advance nuclear nonproliferation analysis

Aug. 21, 2018- 
Artificial neural networks are all around us, deeply embedded in routine functions on the internet. They help online merchants make personalized shopping recommendations, enable social media sites to recognize faces in photos and assist email programs in filtering out spam. Neural networks also have the potential to play a critical role in national security, helping nonproliferation analysts...