CRII: III: Multiresolution Tensor Learning for Scalable and Interpretable Spatiotemporal Analysis

一个可扩展和可解释的时空数据分析框架过去几十年见证了大规模时空数据的爆炸式增长。与此同时，领域专家和政策制定者需要可解释的数据分析工具，以便信任和部署它们。然而，分析时空数据是极具挑战性的，因为这些数据往往表现出复杂的相关性，高维性，并包含多个时空分辨率。为了应对这一挑战，需要新的方法来处理高度相关的数据，将其推广到更高的维度，并在不同的粒度上进行推理。为了应对这些挑战，该项目将开发一个可扩展和可解释的时空数据分析框架。该项目将应用这一框架来解决气候信息学、体育分析和智能交通等领域的挑战性问题。该项目将建立一个大规模的时空数据存储库，并分发开源软件来对研究进展进行基准测试。教育部分将开发面向机器学习和时空分析交叉的新课程。此外，首席研究员计划继续开展外联活动，包括提供教程，在相关会议上组织研讨会。该项目的长期目标是提高时空分析工具的可扩展性和可解释性。首席研究员发起了一个张量学习框架，可以捕获高阶相关性并解决高维问题。该项目将显著扩展该框架，以利用时空数据的多分辨率特性。特别是，首席研究员将开发一个多分辨率张量学习框架，并将该框架集成到现有的模型中，包括潜在因素模型、高斯过程和图神经网络。该方法利用快速张量优化算法来识别多粒度的时空模式。该项目将带来新的技术，以可扩展和可解释的方式自动发现潜在语义，量化不确定性，并从时空数据中学习特征表示。它将进一步有助于我们对先进的优化和统计工具，如多网格优化，基于树的高斯过程和几何深度学习的新兴理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A Framework for Scalable and Interpretable Spatiotemporal Data AnalysisThe past few decades have witnessed an explosion of large-scale spatiotemporal data. At the same time, domain experts and policy makers need data analysis tools that are explainable in order to trust and deploy them. However, analyzing spatiotemporal data is highly challenging, as such data often demonstrates complex correlations, high dimensionality, and contains multiple spatiotemporal resolutions. Meeting this challenge will require new methods that can handle highly correlated data, generalize to higher dimensions, and reason at different granularities. To address these challenges, this project will develop a spatiotemporal data analysis framework that is both scalable and interpretable. The project will apply this framework to tackle challenging problems in climate informatics, sports analytics, and intelligent transportation. This project will establish a large-scale spatiotemporal data repository and distribute open source software to benchmarkresearch progress. The educational component will develop new courses geared towards the intersection of machine learning and spatiotemporal analysis. Additionally, the principal investigator plans to continue outreach activities that involve giving tutorials, organizing workshops at relevant conferences.The long term goal of this project is to improve the scalability and interpretability of spatiotemporal analysis tools. The principal investigator has initiated a framework of tensor learning that can capture higher-order correlations and address high-dimensional issues. This project will significantly expand the framework to exploit the multiresolution nature of spatiotemporal data. In particular, the principal investigator will develop a multiresolution tensor learning framework and integrate this framework into existing models, including latent factor models, Gaussian processes and graph neural networks. This approach leverages fast tensor optimization algorithms to recognize spatiotemporal patterns at multiple granularities. This project will lead to novel techniques to automatically discover latent semantics, quantify uncertainty, and learn feature representations from spatiotemporal data in a scalable and interpretable fashion. It will further contribute to our burgeoning understanding of advanced optimization and statistical tools such as multigrid optimization, tree-based Gaussian processes and geometric deep learning.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.