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Data Scientist Spotlight

Amanda Muyskens

Amanda Muyskens


According to Amanda Muyskens, the best thing about being a statistician is the opportunity to work on—and learn from—unique challenges. She joined the Lab in 2019 after earning a PhD in Statistics from North Carolina State University, and today her research includes Gaussian processes (GP), computationally efficient statistical methods, uncertainty quantification, and statistical consulting. Muyskens is the principal investigator for the MuyGPs project, which introduces a computationally efficient GP hyperparameter estimation method for large data (watch her DSI virtual seminar and the MuyGPs video). Her team used MuyGPs methods to efficiently classify images of stars and galaxies, and they developed an open-source Python code called MuyGPyS for fast implementation of the GP algorithm. Muyskens credits the team dynamic for its success, noting, “We constantly teach each other from our disciplines and achieve things together that wouldn’t have been possible alone.” In 2022, Data Science Summer Institute (DSSI) students contributed to MuyGPs with parameter estimation optimization and an interactive visualization tool. “Students bring a new perspective to the work, and I’m inspired to see them tackle problems in ways that those of us entrenched in the applications may never have considered,” Muyskens says. Most recently, she assumed DSSI co-directorship and began a collaboration with Auburn University data science students.

New Research in AI: HPCwire Award for CogSim Application

Timo, Brian, and Brian standing with the award

The high-performance computing (HPC) publication HPCwire announced LLNL as the winner of its Editor’s Choice award for Best Use of HPC in Energy for applying cognitive simulation (CogSim) methods to inertial confinement fusion (ICF) research. The award was presented on November 14 at SC22, the largest supercomputing conference in the world, and recognizes the team for progress in their ML-based approach to modeling ICF experiments performed at the National Ignition Facility and elsewhere, which has led to the creation of faster and more accurate models of ICF implosions. Emerging at LLNL over the past several years, the CogSim technique uses the Lab’s cutting-edge HPC machines to combine deep neural networks with the massive databases of historical ICF experiments to calibrate the models. Applying CogSim to ICF research has resulted in faster, better-performing models that can predict experimental outcome with higher accuracy than simulations alone and with fewer experiments, according to researchers. Read more at LLNL News.

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