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Upcoming Seminar

Feb. 9: Shawn Newsam from UC Merced. Contact DSI-Seminars [at] llnl.gov (DSI-Seminars[at]llnl[dot]gov) for a WebEx invitation.

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Data Science Meets Fusion (7:39) with Jay Thiagarajan, Luc Peterson, and Kelli Humbird.

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Data Scientist Spotlight

Nicolas Schunck

Nicolas Schunck

Staff Scientist

Nicolas Schunck, a staff scientist with the nuclear data and theory group in LLNL’s Physical and Life Sciences Principal Directorate, researches computational nuclear theory with a particular focus on nuclear fission. Schunck develops models and simulations that predict the properties of short-lived radioactive species in nucleosynthesis mechanisms. He’s also working on a predictive theory of nuclear fission for stockpile stewardship and nuclear forensics programs. Schunck was educated at the University of Strasbourg and worked as a postdoc in the UK, Spain, and at Oak Ridge National Lab before coming to Livermore 13 years ago. A prolific communicator, he lists 66 papers as author or coauthor and edited the textbook Energy Density Functional Methods in Atomic Nuclei. He’s helping mentor five postdocs and always looks forward to working with postdocs and summer interns. “Teaching forces me to question what I know and what I do on a regular basis, which is refreshing,” says Schunck. “Constant reassessment is critical to staying at the cutting edge of research.” Schunck finds his data science work provides valuable perspectives on problems in nuclear science, stating, “By looking at the same old problem in a completely different light, we can come up with innovative solutions.”

New Research

ML Tool Fills in the Blanks for Satellite Light Curves

A black background with vertical stripes that change color from blue at the top and bottom, to purple, to red, to yellow at the center

When viewed from Earth, objects in space are seen at a specific brightness, called apparent magnitude. Over time, ground-based telescopes can track a specific object’s change in brightness. This time-dependent magnitude variation is known as an object’s light curve, and can allow astronomers to infer the object’s size, shape, material, location, and more. Monitoring the light curve of satellites or debris orbiting the earth can help identify changes or anomalies in these bodies. However, light curves are missing a lot of data points. The weather, the season, dust accumulation, time of day, eclipses—these all affect not only the quality of the data, but whether it can be taken at all. Livermore researchers have developed an ML process for light curve modeling and prediction. Called MuyGPs, the process drastically reduces the size of a conventional Gaussian process problem by limiting the correlation of predictions to their nearest neighboring data points, reducing a large linear algebra problem to many smaller, parallelizable problems. This type of ML enables training on more sensitive parameters, optimizing the efficient prediction of the missing data. Read more about MuyGPs.

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high-resolution simulation of the electrical activation map in a human’s heart

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