Data Scientist Spotlight
Jen Caseres
Staff Scientist
Jen Caseres is a staff scientist in LLNL’s Nuclear and Chemical Sciences Division, where she works on chemical and isotopic data analysis for nuclear forensics. She joined the Lab in 2020 after completing an M.S. in Geology at the University of Minnesota and a B.S. in Geochemistry at Caltech. Caseres previously worked in the geology subfield of petrology and used mass spectrometry and electron microscopy to analyze the history and origin of rocks from various settings. Since starting at LLNL, she has developed an interest in applying data science to chemical data for nuclear forensics, which involves the same analytical techniques as petrology—a natural transition. Beyond her job duties, Caseres is also involved in the Girls Who Code program, and she served as a member of the organizing committee for Livermore’s 2024 Women in Data Science (WiDS) event after an insightful experience attending the event in 2023. “LLNL has been a great place to learn data science because of its opportunities for professional development and interdisciplinary collaboration,” she says. “I hope by working with WiDS and other programs like Girls Who Code, I can encourage students and non-data-scientists to start thinking about how data science and programming can help with their data problems.”
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.
