Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
This addresses the problem of interpretability and scalability for scientists using data-driven models, offering a novel approach that scales to millions of samples, though it is incremental in combining existing techniques with new methods.
The paper tackles the challenge of interpreting black-box machine learning models and handling large datasets in scientific applications by presenting a scalable solution for exploring high-dimensional functions, enabling interactive analysis of topological and geometric aspects with demonstrated insights in high-energy-density physics and computational biology.
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely topology aware datacubes, we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.