Topic: AI/ML

Winning the best paper award at PacificVis 2022, a research team has developed a resolution-precision-adaptive representation technique that reduces mesh sizes, thereby reducing the memory and storage footprints of large scientific datasets.

News Item

LLNL participates in the International Parallel and Distributed Processing Symposium (IPDPS) on May 30 through June 3.

News Item

Technologies developed through the Next-Generation High Performance Computing Network project are expected to support mission-critical applications for HPC, AI and ML, and high performance data analytics. Applications could include stockpile stewardship, fusion research, advanced manufacturing, climate research and other open science on future ASC HPC systems.

News Item

Sponsored by the DSI, LLNL’s winter hackathon took place on February 16–17. In addition to traditional hacking, the hackathon included a special datathon competition in anticipation of the Women in Data Science (WiDS) conference on March 7.

News Item

From molecular screening, a software platform, and an online data to the computing systems that power these projects.

Project

LLNL’s cyber programs work across a broad sponsor space to develop technologies addressing sophisticated cyber threats directed at national security and civilian critical infrastructure.

Project

LC sited two different AI accelerators in 2020: the Cerebras wafer-scale AI engine attached to Lassen; and an AI accelerator from SambaNova Systems into the Corona cluster.

Project

This project advances research in physics-informed ML, invests in validated and explainable ML, creates an advanced data environment, builds ML expertise across the complex, and more.

Project

LLNL researchers and collaborators have developed a highly detailed, ML–backed multiscale model revealing the importance of lipids to RAS, a family of proteins whose mutations are linked to many cancers.

News Item

LLNL has established the AI Innovation Incubator (AI3), a collaborative hub aimed at uniting experts from LLNL, industry, and academia to advance AI for scientific and commercial applications.

News Item

An LLNL mathematician and collaborators have developed a machine learning–based technique capable of deriving a mathematical model for the motion of binary black holes from gravitational wave data.

News Item

LLNL will lend its expertise in vaccine research and computing resources to the Human Vaccines Project consortium to aid development of a universal coronavirus vaccine and improve understanding of immune response.

News Item

CASC and the Data Science Institute welcomed a new academic partner to the 2021 Data Science Challenge program: the University of California Riverside campus.

News Item

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 and Data Science Institute.

News Item

UC Merced students engaged with LLNL mentors and peers to address a challenge problem, using machine learning to identify potentially hazardous asteroids that could pose a threat to humanity.

News Item

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.

People Highlight

The 2021 Conference on Computer Vision and Pattern Recognition features two papers co-authored by an LLNL researcher targeted at understanding robust machine learning models.

News Item

The ADAPD program held a virtual meeting to highlight science-based, data-driven analysis work to advance AI innovation and AI-enabled systems to enhance the U.S. nuclear proliferation detection activities.

News Item

New research debuting at ICLR 2021 demonstrates a learning-by-compressing approach to deep learning that outperforms traditional methods without sacrificing accuracy.

News Item

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.

News Item

BUILD tackles the complexities of HPC software integration with dependency compatibility models, binary analysis tools, efficient logic solvers, and configuration optimization techniques.

Project

Led by computational scientist Youngsoo Choi, the Data-Driven Physical Simulation reading group has been meeting biweekly since October 2019. The pandemic almost disbanded the group... until it turned into a virtual seminar series.

News Item

In his opening keynote address at the AI Systems Summit, LLNL CTO Bronis de Supinski described integration of two AI-specific systems to achieve system level heterogeneity.

News Item