CAREER: Discovering Structure in Uncertainty: Using Topology for Interactive Visualization of Uncertainty

University Of Utah, Salt Lake City UT

Investigators

Abstract

In science, ensembles are used to model uncertainties that occur in data from a variety of sources, including errors in measurements, inaccuracies in modeling, and a lack of adequate sampling. Understanding these errors is critical to improving human understanding of phenomena in many areas of science, from urban planning to astrophysics to medicine to weather forecasting, etc. This project investigates new Topological Data Analysis and visualization methods to analyze uncertain data. This will enable scientists to better understand phenomena within their domain by developing new insights and making discoveries more quickly. The techniques will be tested in collaboration with a biomedical engineering research team helping to develop new life-saving treatments for heart attacks and a research team helping to develop technologies that support a safe, clean, and reliable national energy grid. Furthermore, this project will study and advocate for integrating better teaching methodologies, such as peer review, into computer science curricula. The results will be integrated into visualization and computational geometry courses through course materials, such as design mini-challenges, and shared with the educational community through outreach activities, such as pedagogy-themed panels and workshops. To accomplish the goals of the project, the tools of Topological Data Analysis provide a strong theoretical basis for robustly extracting features from ensembles and designing visualizations for performing important uncertainty analysis tasks, including identifying and ranking similarities, identifying and ranking variations, and correlating topological features. This project addresses two important scientific questions: how to effectively use topology to extract features from ensembles; and how to design visualizations for domain experts that efficiently communicate the features. To extract features from an ensemble, the project will investigate new methods of robustly comparing and contrasting the topology of multiple ensemble realizations. Then, in collaboration with domain scientists, it will design new visualization methods for efficiently and effectively comparing and exploring the features and variations within ensembles. The project web site provides additional information and will include access to developed tools, data sets, and educational content. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.