Data Scientist Spotlight
Min Priest
Computer Scientist
Min Priest is a computing scientist in the Computing Directorate’s Center for Applied Scientific Computing (CASC) and 2025 lead of the Data Science Summer Institute (DSSI) internship program. Their time at Livermore began as a CASC graduate student intern in 2018, before they eventually became a postdoctoral researcher and now a staff scientist. Their work lies mostly at the intersection of theoretical computer science—which they studied for their PhD—and high-performance computing, particularly centered around reducing communication in distributed memory algorithms using randomization. They have worked in all areas of this process, from designing algorithms and proving theorems to engineering optimized software, and they have also implemented scalable statistics for a variety of Laboratory projects. “I enjoy this sort of research for several reasons,” Priest said. “One, I find the math I focus on—randomized dimensionality reduction, specifically—to be beautiful and compelling. But I also chase the feeling of taking a blackboard idea and making it run on a huge computer; it still feels like magic every time something works.”
Recent Research
AI-Boosted Exascale Fusion Simulations
A groundbreaking multidisciplinary team of LLNL researchers is combining the power of exascale computing with AI, advanced workflows, and GPU acceleration to advance scientific innovation and revolutionize digital design. The project, called ICECap (Inertial Confinement on El Capitan), is a transformative approach to inertial confinement fusion design optimization targeted primarily for El Capitan, the National Nuclear Security Administration's first exascale computer.
“With ICECap, we're trying to see how we can leverage AI to really change the way we do scientific discovery,” said principal investigator Luc Peterson. “We have supercomputers that can do fantastic simulations, but how can we use AI to help us take advantage of them to find new things? We’re doing this on El Capitan because we think we’re at the point where we can actually do both breadth and depth in computing, so you can search lots of parameter spaces to find what you're looking for and do it all in extremely high fidelity.” Read more via LLNL News.
