在许多“现代”应用领域中，收集具有结构不能很好地用简单标量，向量或矩阵表示的数据的情况越来越普遍。例如，对于每个研究对象，可能会观察到随时间变化的曲线，复杂的形状甚至是多向阵列或图形。无法使用传统的统计方法来分析此类对象数据，需要设计新的方法来适当地表征数据的几何形状，同时启用统计推断。这种方法的发展本质上是跨学科的。定义数据的适当数学表示形式（不过分简化几何和拓扑结构）需要具备功能分析知识。开发模型和方法以使这些模型适合数据并进行推理需要大量的统计技能。此外，计算，数据处理和存储通常令人望而生畏，需要熟
Increasingly in many ?modern? application areas, it has become common to collect data that have a structure that is not well represented by a simple scalar, vector or matrix. For example, for each subject under study, one may observe a curve over time, a complex shape or even a multiway array or graph. Such object data cannot be analyzed using traditional statistical methods, and new approaches need to be designed that appropriately characterize the geometry of the data, while enabling statistical inferences. The development of such methods is inherently inter-disciplinary. Defining appropriate mathematical representations of the data which do not overly simplify the geometric and topological structure requires knowledge of functional analysis. Developing models and methodology for fitting these models to data and performing inferences requires substantial statistical skills. In addition, computation and data processing and storage are often daunting, requiring adeptness with design of efficient algorithms and skills in computer science. Fortunately, Duke is particularly inter-disciplinary with faculty who are experts in each of these complementary areas of ?geometric data science?.