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

Data science has become an essential discipline paving the path of LLNL's key program areas, and the Laboratory is home to some of the largest, most unique, and most interesting data and supercomputers in the world. The DSI acts as the central hub for all data science activity—in areas of artificial intelligence, big-data analytics, computer vision, machine learning, predictive modeling, statistical inference, uncertainty quantification, and more—at LLNL working to help lead, build, and strengthen the data science workforce, research, and outreach to advance the state-of-the-art of our nation's data science capabilities. Read more about the DSI.

Data Scientist Spotlight

Kathleen Schmidt

Kathleen Schmidt

Applied Statistician

As an applied statistician who enjoys tackling interesting problems, Kathleen Schmidt is never bored. “Nearly every field with a quantitative question can benefit from a statistician, so we get to explore a wide variety of science applications,” she says. Schmidt works primarily on two projects: one with messy physics reaction history data collected from older technology, and another where statistical modeling helps optimize materials strength experiments. Her recent publications include modeling for radiation source localization and material behavior in extreme conditions. During 2019–2021, Schmidt served as technical coordinator for the DSI’s seminars and transitioned the series to a virtual format in 2020. She recently spoke at the 4th Annual Reaction History Workshop and has presented at the Lab’s regional Women in Data Science event. She states, “Each data scientist has an individual area of expertise. In coming together as a community, we all have something to contribute.” Schmidt earned a PhD in Applied Mathematics from North Carolina State University before joining the Lab as a postdoctoral researcher in 2016 and converting to full-time staff in 2018.

New Research in AI: 4D Computed Tomography Reconstructions

three rows of thoracic CT images with slight variations
4D CT of material deformation at beginning and end stages (left) and human thoracic cavity at three different breathing phases (right). From top to bottom: ground truth, scene reconstruction with the research team's novel 4D CT method, and existing state-of-the-art method.

Computed tomography (CT) is a type of x-ray imaging technology with a range of applications for clinical diagnosis, non-destructive evaluation in industry, baggage inspection, and cargo screening. CT scanners capture a sequence of angles around an object. Reconstruction algorithms then estimate the scene from these measured images. Together with Arizona State University colleagues, LLNL researchers have designed a reconstruction pipeline using implicit neural representations (INR, i.e., use of deep learning techniques for optimization) with a novel parametric motion field warping to perform 4D-CT reconstruction of rapidly changing scenes of moving or deformable objects with limited view samplings. The team’s paper, titled “Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields,” was accepted to the 2021 International Conference on Computer Vision (ICCV).

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Data Science Challenge welcomes UC Riverside

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