The term Shape Analytics describes our ever-growing suite of techniques for the data-driven analysis of real-world problems.

Our methods augment traditional data analysis with sophisticated concepts from contemporary mathematics. Beginning with the cutting-edge area of topological data analysis [TDA], our toolkit has expanded to incorporate ideas from geometry, measure theory, information theory, graph theory, and analysis.

GDA specializes in extracting shape descriptors from non-traditional data objects, such as signals, sets of signals, images, and high-dimensional point clouds. These shape descriptors are parsimonious, which avoids the well-known ``curse of dimensionality’’ that often reduces the real-world impact of machine-learning methods. They are also provably robust to uncertainty in measurement errors.