Data Science in the News

LLNL physicist wins Young Former Student award

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

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...

Model for COVID-19 drug discovery a Gordon Bell finalist

A machine learning model developed by a team of LLNL scientists to aid in COVID-19 drug discovery efforts is a finalist for the Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research. Using Sierra, the world’s third fastest supercomputer, LLNL scientists produced a more accurate and efficient generative model to enable COVID-19 researchers to produce novel compounds...

DOE announces five new energy projects at LLNL

The DOE today announced two rounds of awards for the High Performance Computing for Energy Innovation Program HPC4EI), including five projects at LLNL. HPC4EI connects industry with the computational resources and expertise of the DOE national laboratories to solve challenges in manufacturing, accelerate discovery and adoption of new materials and improve energy efficiency. The awards were...

Machine learning speeds up and enhances physics calculations

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...

Advancing healthcare with data science (VIDEO)

This video provides an overview of projects in which data scientists work with domain scientists to address major challenges in healthcare. To help fight the COVID-19 pandemic, researchers are developing computer models to search for potential antibody and antiviral drug treatments, sharing a data portal with scientists and the general public, and analyzing drug compounds via a novel text...

Software integration of AI accelerators in HPC (VIDEO)

In this episode of Next Platform TV, LLNL's Brian Van Essen talks about AI accelerator integration in HPC systems and software stacks. His interview begins at timestamp 27:57. Watch on YouTube.

LLNL papers accepted into prestigious conference

Two papers featuring LLNL scientists were accepted in the 2020 International Conference on Machine Learning (ICML), one of the world’s premier conferences of its kind. Read more at LLNL News.

Carnegie Live: high energy density science and AI (VIDEO)

In this Carnegie Live video, Seiichi Shimasaki, Science Counselor for the Japanese embassy in the U.S., described a multiyear science research program (nicknamed the “Moonshot”) to develop new technologies that help solve some of society’s most pressing challenges. He explained that the Government of Japan was looking for a data science program to mentor young scientists, which led to the...

AI identifies change in microstructure in aging materials

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.

AI hardware for future HPC systems (VIDEO)

This interview with Brian Spears, who leads cognitive simulations at LLNL, covers the current state of evaluation of AI chips and how those will mesh with existing and future HPC systems. Watch on YouTube.

COVID-19 research goes public through new portal

A new online data portal is making available to the public a wealth of data LLNL scientists have gathered from their ongoing COVID-19 molecular design projects, particularly the computer-based “virtual” screening of small molecules and designed antibodies for interactions with the SARS-CoV-2 virus for drug design purposes. The portal houses a wealth of data LLNL scientists have gathered from...

Interpretable AI in healthcare (PODCAST)

LLNL's Jay Thiagarajan joins the Data Skeptic podcast to discuss his recent paper "Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models." The episode runs 35:50. Listen at Data Skeptic.

Faces of STEM: Brenda Ng (VIDEO)

In this video, LLNL machine learning scientist Brenda Ng explains why she loves her job in STEM, what advice she has for others, what inspired her to go into STEM, and what she does in her free time. Watch on YouTube.

The incorporation of machine learning into scientific simulations at LLNL (VIDEO)

In this video from the Stanford HPC Conference, Katie Lewis presents "The Incorporation of Machine Learning into Scientific Simulations at Lawrence Livermore National Laboratory." Read more and watch the video at insideHPC.

New ML platform generates novel COVID-19 antibody sequences for experimental testing

LLNL researchers have identified an initial set of therapeutic antibody sequences, designed in a few weeks using machine learning and supercomputing, aimed at binding and neutralizing SARS-CoV-2, the virus that causes COVID-19. The research team is performing experimental testing on the chosen antibody designs. Read more at LLNL News.

Upgrades for LLNL supercomputer from AMD, Penguin Computing aid COVID-19 research

Under a new agreement, AMD will supply upgraded graphics accelerators for LLNL’s Corona supercomputing cluster, expected to nearly double the system’s peak compute power. The system will be used by scientists through the public/private COVID-19 HPC Consortium, and by LLNL researchers, who are working on discovering potential antibodies and anti-viral compounds for SARS-CoV-2, the virus that...

Using data to build a secure future (PODCAST)

On the Hidden in Plain Sight podcast, LLNL director Bill Goldstein explains how the Lab crunches data to shape the future. Listen at Mission.org.

LLNL creates web resources to aid in fight against COVID-19

LLNL is fully committed to helping protect the U.S. from COVID-19 and to speed the recovery of those affected. As a world-class research institute, we have considerable infrastructure, unique research capabilities and a dedicated team of scientists and engineers supporting the fight against the COVID-19 pandemic. Our current COVID-19 research and response activities are focused on four broad...

New partnership to unleash U.S. supercomputing resources in the fight against COVID-19

The White House announced the launch of the COVID-19 High Performance Computing Consortium to provide COVID-19 researchers worldwide with access to the world’s most powerful high performance computing resources that can significantly advance the pace of scientific discovery in the fight to stop the virus. Read more at LLNL News.