Topic: AI/ML

LLNL researchers collaborated with Washington University in St. Louis to devise a state-of-the-art ML–based reconstruction tool for when high-quality computed tomography data is in low supply.

LLNL is participating in the 35th annual Supercomputing Conference (SC23), which will be held both virtually and in Denver on November 12–17, 2023.

The Institute of Electrical and Electronics Engineers (IEEE), the world’s largest technical professional organization, has elevated LLNL staff member Bhavya Kailkhura to the grade of senior member within the organization.

Merlin is an open-source workflow orchestration and coordination tool that makes it easy to build, run, and process large-scale workflows.

Cindy Gonzales earned a bachelor’s degree and master’s degree and changed careers—all while working at the Lab. Meet the deputy director of LLNL’s Data Science Institute.

CASC computational mathematician Andrew Gillette has always been drawn to mathematics and says it’s about more than just crunching numbers.

Using explainable artificial intelligence techniques can help increase the reach of machine learning applications in materials science, making the process of designing new materials much more efficient.

Highlights include MFEM community workshops, compiler co-design, HPC standards committees, and AI/ML for national security.

In a time-trial competition, participants trained an autonomous race car with reinforcement learning algorithms.

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.

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

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

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.

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.

Highlights include power grid challenges, performance analysis, complex boundary conditions, and a novel multiscale modeling approach.

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.

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

Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.

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

Three papers address feature importance estimation under distribution shifts, attribute-guided adversarial training, and uncertainty matching in graph neural networks.

StarSapphire is a collection of scientific data mining projects focusing on the analysis of data from scientific simulations, observations, and experiments.

Lawrence Livermore National Lab has named Stefanie Guenther as Computing’s fourth Sidney Fernbach Postdoctoral Fellow in the Computing Sciences. This highly competitive fellowship is named after LLNL’s former Director of Computation and is awarded to exceptional candidates who demonstrate the potential for significant achievements in computational mathematics, computer science, data science, or scientific computing.

Highlights include response to the COVID-19 pandemic, high-order matrix-free algorithms, and managing memory spaces.

Rafael Rivera-Soto is passionate about artificial intelligence, deep learning, and machine learning technologies. He works in LLNL’s Global Security Computing Applications Division, also known as GSCAD.

ADAPD integrates expertise from DOE national labs to analyze growing global data streams and traditional intelligence data, enabling early warning of nuclear proliferation activities.