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.
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, the virtual event brought together more than 500 attendees from the Department of Energy (DOE) complex, commercial companies, professional societies, and academia.
A newly funded project involving co-principal investigator and LLNL computer scientist Ignacio Laguna will examine one of the major challenges as supercomputers become increasingly heterogenous—the numerical aspects of porting scientific applications to different HPC platforms.
From studying radioactive isotope effects to better understanding cancer metastasis, the Laboratory’s relationship with cancer research endures some 60 years after it began, with historical precedent underpinning exciting new research areas.
A new career panel series that kicked off in June continued on August 10 with a session featuring former LLNL interns who converted to full-time employment at the Lab. Moderator Mary Silva was joined by panelists from the Computing and Engineering Directorates.
LLNL and Purdue are partnering to speed up drug design using computational tools under the Accelerating Therapeutic Opportunities in Medicine project. LLNL researcher Jonathan Allen mentored students and two teaching assistants, introducing them to computationally driven drug discovery and designing predictive models for drug candidates.
Held virtually on July 15, our fifth annual Developer Day featured lightning talks, a technical deep dive, “quick takes” on remote-development resources, presentations about career paths, and a career development panel discussion.
In this episode (32:00), LLNL's Jeff Hittinger talks about scientific success, leadership, and the tricks he’s cultivated for communicating science to broader audiences through the Livermore Ambassador Lecture series.
The Department of Energy announced awards of $3.7 million for 13 new High Performance Computing for Energy Innovation (HPC4EI) projects, including a collaboration involving LLNL targeted at improving CO2 conversion.
More than 100 LLNL staff and students gathered virtually for the first session of a new career panel series inspired by the annual Women in Data Science conference and sponsored by the Data Science Institute.
LLNL engineers have demonstrated that aerodynamically integrated vehicle shapes decrease body-axis drag in a crosswind, creating large negative front pressures that effectively “pull” the vehicle forward against the wind, much like a sailboat.
In experiments performed at the University of Wisconsin-Madison, researchers found that fluctuations in the electrical charge of multiple quantum bits (qubits) can be highly correlated. The team also linked tiny error-causing perturbations in the qubits’ charge state to the absorption of cosmic rays.
Meeting virtually three times per week, 22 UC Merced students engaged with LLNL mentors and peers to address a real-world challenge problem, using machine learning to identify potentially hazardous asteroids that could pose an existential threat to humanity.
Using the Miranda code and the Ruby supercomputer, an LLNL team has taken a closer look at how nuclear weapon blasts close to the Earth’s surface create complications in their effects and apparent yields.
On a recent video episode of The Data Standard Podcast, biostatistician Nisha Mulakken discusses the Lawrence Livermore Microbial Detection Array (LLMDA) system, which has detection capability for all variants of SARS-CoV-2.
LLNL was honored by the American Indian Science and Engineering Society (AISES) Winds of Change magazine as one of the Top 50 STEM Workplaces in 2021, as an organization setting the standard for indigenous STEM professionals.