Data Scientist Spotlight
Aisha Dantuluri is a staff data scientist in LLNL’s Applications, Simulations, and Quality division. Her projects mainly contribute to the Lab’s Weapon Physics and Design program. She uses machine learning (ML) for image processing to make better quantitative use of simulated and experimental radiographic data for stockpile certification. She also works on automating time series analysis in the Diagnostics Development Group, which involves jump off detection in photo Doppler velocimetry data. Data science inspires Dantuluri due to its collaborative nature and range of applications. “Everybody is open to sharing knowledge, and I think that makes data science a fun thing to work on. You can pick any problem in any field, and they probably need a data scientist,” she said. Since she began working at LLNL in 2020, Dantuluri has sought to improve the sense of community amongst data scientists in different organizations. She is the current ML working group coordinator for WPD and hopes to foster more involvement with the Strategic Deterrence organization’s data science community in the future. Dantuluri earned a master’s degree in computational science at UC San Diego and a bachelor’s degree in engineering physics at the Indian Institute of Technology, Hyderabad.
ML Tool Fills in the Blanks for Satellite Light Curves
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.