LLNL has established the AI Innovation Incubator (AI3), a collaborative hub aimed at uniting experts in artificial intelligence (AI) from LLNL, industry and academia to advance AI for large-scale scientific and commercial applications.
The MFEM software library provides high-order mathematical algorithms for large-scale scientific simulations. An October workshop brought together MFEM’s global user and developer community for the first time.
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 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.
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.
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.
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.
Computing relies on engineers like Stephanie Brink to keep the legacy codes running smoothly. “You’re only as fast as your slowest processor or your slowest function,” says Brink, who works in CASC. By analyzing a legacy code’s performance, Brink and her team can reduce the amount of time it takes to run and allow for more critical science to be accomplished.
Coinciding with International Women’s Day on March 8, LLNL’s 4th Women in Data Science (WiDS) regional event brought women together to discuss successes, opportunities and challenges of being female in a mostly male field.
Nisha Mulakken is advancing COVID-19 R&D and mentoring the next generation. “The opportunities we are exposed to early in our careers can shape the limits we place on ourselves and our approaches to challenges we encounter throughout our careers,” she says.