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Simulations Reveal An Unusual Death for Ancient Stars


Certain primordial stars—those between 55,000 and 56,000 times the mass of our Sun, or solar masses—may have died unusually. In death, these objects—among the Universe’s first-generation of stars—would have exploded as supernovae and burned completely, leaving no remnant black hole behind. » Read More

To Bridge LEDs’ Green Gap, Scientists Think Small


Computer simulations suggest that the strong quantum confinement in nanowires of indium nitride (InN) could hold the key to efficient green LEDs. Using ParaView, Burlen Loring assisted researchers by visualizing their simulations of a 1 nanometer-wide InN wire showing the distribution of an electron around a positively charged “hole," the mechanism that makes LEDs glow. » Read More

Image Recognition to Speed Cervical Cancer Screenings


Daniela Ushizima of the Visualization and Analytics Group working with Brazilian collaborators won a biomedical imaging challenge using pattern recognition algorithms developed to characterize new materials by the Department of Energy’s Center for Applied Mathematics for Energy Research Applications (CAMERA). Their method for identifying cells in Pap smears used to detect cervical cancer was judged to be the fastest and most accurate. » Read More


The Machine Learning & Analytics Group conceives, designs, and implements new methods in high-performance machine learning, data and image analytics, computational geometry and topology, and visualization technologies. Our work involves a mix of theoretical research and applied research. The group works in close collaboration with scientific partners to identify and address challenging, large-scale, data-rich problems emerging from simulations, experiments, and observations.

Group Leader: Wes Bethel