Topic: Data Science

Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.

At just 5 years old, Marisol Gamboa, the oldest of six siblings to Mexican immigrants, decided she was definitely going to college.

Newly developed mathematical techniques reveal important tools for data mining analysis.

Drawing from data mining, image and video processing, statistics, and pattern recognition, these computational tools improve the way scientists extract useful information from data.

This project's techniques reduce bandwidth requirements for large unstructured data by making use of data compression and optimizing the layout of the data for better locality and cache reuse.

LLNL computer scientists use machine learning to model and characterize the performance and ultimately accelerate the development of adaptive applications.

Researchers are developing enhanced computed tomography image processing methods for explosives identification and other national security applications.

New platforms are improving big data computing on Livermore’s high performance computers.

LLNL and University of Utah researchers have developed an advanced, intuitive method for analyzing and visualizing complex data sets.

hzip 1.0.1 is a C++ library for lossless compression of structured and unstructured meshes composed of cells with hypercube topology.

The flourishing of simulation-based scientific discovery has also resulted in the emergence of the UQ discipline, which is essential for validating and verifying computer models.

This genome sequencing technology helps accelerate the comparison of genetic fragments with reference genomes and improve the accuracy of the results as compared to previous technologies.

Jeene Villanueva develops enterprise modeling tools that help DOE decision makers gain insight into the challenging problems faced by the U.S. nuclear weapons complex.