Topic: Data Science

LLNL researchers have posters and workshop papers accepted to the 13th International Conference on Learning Representations on April 24–28.

LLNL researchers have created new, optimized indices of the nt database that simplify how scientists classify microorganisms found in various samples, significantly improving the ability to identify and understand the myriad microorganisms that inhabit our world.

Researchers from LLNL, in collaboration with other leading institutions, have successfully used an AI-driven platform to preemptively optimize an antibody to neutralize a broad diversity of SARS-CoV-2 variants.

More than 3,200 LLNL employees participated in the first-ever aiEDGE for Innovation Day on March 26—an event aimed at empowering and equipping the Lab’s workforce to integrate AI into their daily work.

A study led by LLNL scientists is providing new insights into the complex interactions between proteins and cell membranes, combining detailed molecular simulations and large-scale models.

In a recent study published in the Astrophysical Journal, LLNL researchers developed an innovative approach to map cosmic shear using linear algebra, statistics, and HPC.

The February 28 event brought together over 1,400 Department of Energy scientists across multiple sites to explore how cutting-edge AI models could transform scientific research.

Increased resource utilization is one goal of new architectures. At Livermore Computing, these include AI accelerators such as Samba Nova and Cerebras systems and El Capitan's Rabbits.

Over the next three years, CASC researchers and collaborators will integrate LLMs into HPC software to boost performance and sustainability.

Highlights include ML techniques for computed tomography, a scalable Gaussian process framework, safe and trustworthy AI, and autonomous multiscale simulations.

The latest issue of LLNL's magazine explains how the world’s most powerful supercomputer helps scientists safeguard the U.S. nuclear stockpile.

LLNL's Bruce Hendrickson joins other HPC luminaries in this op-ed about the future of the field.

The latest episode of the Big Ideas Lab podcast investigates the use of artificial intelligence for drug discovery and other uses.

Todd Gamblin has a well-deserved reputation in the HPC software community as a passionate engineer who enjoys rolling up his sleeves and diving into technical problems. It’s not a stretch to see how he got hooked on HPC.

The team used a Bayesian approach to quantify metal strength uncertainty with tantalum and two common explosive materials and integrated it into a coupled metal/high-explosive model.

Presented last fall at a conference, a new approach to software binary analysis incorporates large-scale training data and hierarchical embeddings.

The DarkStar inverse design technique blends AI, machine learning, and advanced hydrodynamics simulations to optimize science and engineering solutions starting from the final state.

This interview with HPC-AI Vanguard Kathryn Mohror covers her thoughts on teamwork, her projects, the field, and more.

SC24, held recently in Atlanta, was a landmark event, setting new records and demonstrating LLNL's unparalleled contributions to HPC innovation and impact.

Drawing more than 300 attendees, this year’s “D3” workshop focused on tackling pressing data challenges in nuclear security, energy and collaborative scientific discovery, and featured a host of talks, presentations and panels.

The Generative Unconstrained Intelligent Drug Engineering (GUIDE) program accelerates development of medical countermeasure candidates to redefine biological defense.

LLNL is participating in the 36th annual Supercomputing Conference (SC24) in Atlanta on November 17–22, 2024.

AMS is a machine learning solution embedded into scientific applications to automatically replace fine-scale simulations with ancillary models.

Learn about the game-changing potential of El Capitan and discover how it will not only transform HPC and AI but also revolutionize scientific research across multiple domains.

A groundbreaking multidisciplinary team is combining the power of exascale computing with AI, advanced workflows, and GPU acceleration to advance scientific innovation and revolutionize digital design.