Our mission at the Data Science Institute (DSI) is to enable excellence in data science research and applications across LLNL's core missions.

Data Scientist Spotlight

Ruben Glatt

Ruben Glatt

Senior Staff Researcher

Working at LLNL gives Ruben Glatt, a senior staff researcher in machine learning, the opportunity to solve globally important problems. He currently leads a Laboratory Directed Research and Development feasibility study using reinforcement learning (RL) to investigate energy-efficient transportation and, in other projects, applies generative models and domain concepts to improve trust in model predictions. “Keeping up with the developments in the fields of data science and machine learning can pose a challenge, yet the recent progress made in these areas holds immense potential to revolutionize energy generation, healthcare, and other industries,” he explains. “As we embark on a new era where artificial intelligence holds the power to master an increasing number of processes, it is my aspiration to ensure that research remains aligned with humanistic values, so that we can not only fully realize the benefits of these advancements, but also minimize any potential existential risks.” In 2022, Glatt chaired the Lab’s Center for Advanced Signal and Image Sciences (CASIS) workshop and was part of the research team whose deep symbolic optimization method won the first-ever worldwide symbolic regression competition. He has presented his research at numerous venues including premier machine learning conferences, and contributed a chapter on efficient RL to the Federated and Transfer Learning book, published by Springer in 2022. Glatt joined the Lab in 2019 after completing a PhD in Computer Engineering with a focus on knowledge transfer in RL.

New Research in AI: HPC and CogSim Aid Fusion Ignition Breakthrough

artist's rendering of lasers hitting the target

On December 5th, the research team at LLNL's National Ignition Facility (NIF) achieved a historic win in energy science: for the first time ever, more energy was produced by an artificial fusion reaction than was consumed—3.15 megajoules produced versus 2.05 megajoules in laser energy to cause the reaction. High-performance computing was key to this breakthrough (called ignition), and HPCwire recently had the chance to speak with Brian Spears, deputy lead modeler for inertial confinement fusion (ICF) at the NIF, about HPC’s role in making this fusion ignition a reality. Spears said there were essentially two pieces to their predictive capability. First, their fundamental design capability—using hundreds of thousands of lines of radiation hydrodynamics code to run massive simulations of fusion reactions on leadership-class supercomputers. Second: cognitive simulation, or CogSim. Read more at HPCwire.

More AI news >>

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