News

Our 2023 Annual Report

Just Issued: LLF’s 2023 Annual Report.

Local Leaders Named to Livermore Lab Foundation Board of Directors

Long-time community leader Lori Souza and University of California National Laboratories (UCNL) management executive June Yu have joined the board of directors of the Livermore Lab Foundation (LLF), effective January 2024.

Just Released: Roads to Removal

A first-of-its-kind national scale analysis, outlining pathways for carbon dioxide (CO2) removal to help the U.S. reach its climate goals.

LLF, LLNL, & CSUB Working to Advance Clean Energy

In September 2023, LLF, LLNL, and California State University Bakersfield (CSUB) signed a MOU agreement to collaborate on advanced and clean-energy technologies, research opportunities and community partnerships that have the potential to shape the future of energy in the state and bring high-quality jobs to the region.

LLF & LLNL Awarded UC Climate Grant

Supported by a ~$1M grant from the University of California (UC), LLF is working with LLNL to scale up climate technology solutions and promote research, education, and entrepreneurship around the new carbon economy by investing in student fellows and innovative research teams.

LLF – SIAM Undergraduate Fellowship

During the summer of 2023, LLF partnered with the Society for Industrial and Applied Mathematics to support undergraduate students seeking to advance their applied mathematics skills in a world-class research facility addressing some of society’s greatest scientific and technological challenges.

LLF’s Fusion Fellow

The Foundation believes in creating pathways and opportunities for the next generation to work in fusion to fuel long-term success in this research area. This year, LLF launched an inaugural Fusion Fellowship for students to work on the NIF team at LLNL, with Jacob Crouch as the first fellow!

LLF’s Data Science Fellow

LLF Fellow Ryan Lee built a machine learning algorithm to craft a replica of the human gut microbiome using data analysis and gene classification. The goal was to strengthen and accelerate neuro-disease prediction capabilities without the need for more costly clinical tests with human subjects.