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With the rapid adoption of machine learning techniques for large-scaleapplications in science and engineering comes the convergence of two grandchallenges in visualization. First, the utilization of black box models (e.g.,deep neural networks) calls for advanced techniques in exploring andinterpreting model behaviors. Second, the rapid growth in computing hasproduced enormous datasets that require techniques that can handle millions ormore samples. Although some solutions to these interpretability challenges havebeen proposed, they typically do not scale beyond thousands of samples, nor dothey provide the high-level intuition scientists are looking for. Here, wepresent the first scalable solution to explore and analyze high-dimensionalfunctions often encountered in the scientific data analysis pipeline. Bycombining a new streaming neighborhood graph construction, the correspondingtopology computation, and a novel data aggregation scheme, namely topologyaware datacubes, we enable interactive exploration of both the topological andthe geometric aspect of high-dimensional data. Following two use cases fromhigh-energy-density (HED) physics and computational biology, we demonstrate howthese capabilities have led to crucial new insights in both applications.

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications