Evaluating trust and safety of large language models

Accepted to the 2024 International Conference on Machine Learning, two Livermore papers examined trustworthiness—how a model uses data and makes decisions—of large language models, or LLMs. In “TrustLLM: Trustworthiness in Large Language Models,” Bhavya Kailkhura and collaborators from universities and research organizations around the world developed a comprehensive trustworthiness...

Signal and image science community comes together for annual workshop

Nearly 150 members of the signal and image science community recently came together to discuss the latest advances in the field and connect with colleagues, friends, and potential collaborators at the 28th annual Center for Advanced Signal and Image Science (CASIS) workshop. The event featured more than 50 technical contributions across six workshop tracks and a parallel tutorials session...

Consulting service infuses Lab projects with data science expertise

A key advantage of LLNL’s culture of multidisciplinary teamwork is that domain scientists don’t need to be experts in everything. Physicists, chemists, biologists, materials engineers, climate scientists, computer scientists, and other researchers regularly work alongside specialists in other fields to tackle challenging problems. The rise of Big Data across the Lab has led to a demand for...

Patent applies machine learning to industrial control systems

An industrial control system (ICS) is an automated network of devices that make up a complex industrial process. For example, a large-scale electrical grid may contain thousands of instruments, sensors, and controls that transfer and distribute power, along with computing systems that capture data transmitted across these devices. Monitoring the ICS network for new device connections, device...

From plasma to digital twins

LLNL's Nondestructive Evaluation (NDE) group has an array of techniques at its disposal for inspecting objects’ interiors without disturbing them: computed tomography, optical laser interferometry, and ultrasound, for example, can be used alone or in combination to gauge whether a component’s physical and material properties fall within allowed tolerances. In one project, the team of NDE...

Understanding the universe with applied statistics (VIDEO)

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...

LLNL to cooperate with University of Utah's one oneAPI Center of Excellence

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...

CASC team wins best paper at visualization symposium

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

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

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...

COVID-19 R&D: Computing responds to pandemic

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...

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

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...

Lab-led effort one of nine DOE-funded data reduction projects

An LLNL-led effort in data compression was one of nine projects recently funded by the DOE for research aimed at shrinking the amount of data needed to advance scientific discovery. Under the project—ComPRESS: Compression and Progressive Retrieval for Exascale Simulations and Sensors—LLNL scientists will seek better understanding of data-compression errors, develop models to increase trust in...

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

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’...

Virtual seminar series explores data-driven physical simulations

The rapidly growing fields of artificial intelligence (AI) and machine learning (ML) have become cornerstones of LLNL’s data science research activities. The Lab’s scientific community regularly publishes advancements in both AI/ML applications and theory, contributing to international discourse on the possibilities of these compelling technologies. The large volume of AI/ML scientific...

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...

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...

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.

New partnership results in increased access to compelling 'real world data'

Through a new partnership between the UC San Diego Library, Halıcıoğlu Data Science Institute (HDSI), and LLNL's Data Science Institute, UCSD library patrons can now access and analyze two new “real world” data sets from LLNL. The Open Data Initiative collection shares some of LLNL’s challenging and unique data sets, which range in complexity from large-scale, domain-specific simulated data...

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...