Aaron N. Bryden

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We investigate how to represent the resulting multivariate information and multidimensional uncertainty by developing and applying candidate visual techniques. Although good techniques exist for visualizing many data types, less progress has been made on how to display uncertainty and multivariate information - this is especially true as the dimensionality(More)
Current methods of materials development, relying mostly on experimental tests, are slow and expensive, often taking over a decade and costing many millions of dollars to develop and certify new materials for critical applications. Finding new approaches for materials development is essential. Moreover, it will be increasingly important for materials(More)
Ions with similar time-of-flights (TOF) can be discriminated by mapping their kinetic energy. While current generation position-sensitive detectors have been considered insufficient for capturing the isotope kinetic energy, we demonstrate in this paper that statistical learning methodologies can be used to capture the kinetic energy from all of the(More)
Understanding the impact of noise and incomplete data is a critical need for using atom probe tomography effectively. Although many tools and techniques have been developed to address this problem, visualization of the raw data remains an important part of this process. In this paper, we present two contributions to the visualization of data acquired(More)
Not all virtual reality applications today require the power or expense of single large visualization “supercomputers”. Factors such as frame rate and polygon count have a major impact upon the performance of a VR application. Increasingly, low cost commodity consumer electronics and computing technology are becoming powerful enough to present an acceptable(More)
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