Visualization plays a crucial role in research and industry by offering users a graphical interface to their data that affords them an intuitive basis for interpretation, assessment and decision making. Yet, the rapidly growing size, dimensionality, and multi-scale complexity of the produced scientific data create a pervasive analysis challenge that is not properly addressed by existing visualization technology. In particular, the investigation of inherently multivariate and multifield physical problems is typically reduced to the visualization of a single scalar or vector field, thereby neglecting a wealth of important information.
The PI will address the limitations of the current state of the art by pioneering a comprehensive approach for the efficient visual analysis of large-scale datasets. Central to the proposed approach is a novel definition of geometric saliency that will permit the automatic identification of remarkable manifolds in scientific data. To that end, the PI will unify and subsume a variety of concepts from mathematics and computer vision to create a principled and versatile model for the structural analysis and visual representation of multifield problems. Innovative data structures and sparse sampling strategies leveraging parallel architectures will be devised to enable the efficient processing of large and multivariate datasets at scale. Lastly, these computational foundations will power a user-centric visual analysis framework that the PI will assess in the context of multidisciplinary collaborations spanning fluid dynamics, materials engineering, high-energy physics, and cardiovascular research.
This research effort will benefit the scientific community by contributing a rigorous and scalable framework for the effective analysis and visualization of computational or measured datasets across a broad range of scientific problems. The PI will distribute the created software artifacts through an open source web portal and integrate them in leading visualization tools to facilitate their dissemination. The PI will organize tutorials and workshops at premier conferences to raise the awareness of the user community about the developed technology and he will provide benchmark datasets and sample results to promote a collaborative effort in the visualization community. Beyond research, the PI will create new courses at both the undergraduate and graduate levels to expose students to the critical importance of data analysis in science and engineering and to the role of advanced visualization in this context. Finally, these education activities will naturally complement an outreach effort toward underrepresented minorities and local K-12 programs.
