Data Scientist Spotlight
Luke Jaffe
Luke Jaffe is a data scientist in the Global Security Computing Applications Division, where he applies computer vision to a variety of areas such as facial recognition, adversarial machine learning, battery defect detection, and power grid infrastructure for increased resilience. “I’m working on recognizing things at microscopic scale in material defect images, but also at macroscopic scale in images from handheld cameras and satellites,” Jaffe said. “The cool part is that you can use the same modeling techniques at these vast differences in scale.” Jaffe began his time at LLNL as an intern in 2015, joined full-time in 2016, and completed his PhD through the Employee Tuition Assistance Program while continuing to work. Through word of mouth, he often supports and advocates for other employees seeking to make use of this valuable program. This March, he is representing DSI at the Winter Conference on Applications of Computer Vision. “It’s important to have people from Livermore at top computer vision conferences, as it is an overlooked part of AI right now compared to large language models,” he said.
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.
