CAREER: Modeling and Inference for Large Scale Spatio-Temporal Data

关键的可持续性挑战，如减轻贫困、气候变化和粮食安全，涉及规模和复杂性独特的全球现象。我们的全球传感能力——从遥感到众包——正变得越来越经济和准确。这些最近的技术发展正在创造新的时空数据流，其中包含与可持续发展目标有关的大量信息。然而，可操作的见解不能轻易提取，因为数据的庞大规模和非结构化性质阻碍了传统的分析技术。这个五年职业发展计划是一个综合研究、教育和推广计划，重点是开发新的人工智能技术，从大规模时空数据中提取可操作的见解。这些技术有可能产生准确、廉价和高度可扩展的模型，为研究和政策提供信息。该项目的研究目标是开发新的建模和算法框架，以帮助解决涉及时空数据的全球可持续性挑战。本研究将以独特的方式整合图形模型和表征学习的思想，开发新的复杂时空现象预测模型，提高其整体性能。将开发利用各种形式的先验领域知识（包括时空依赖关系和不同数据模式之间的关系）从未标记数据中学习的新方法。为了大规模地学习模型并进行预测，该项目还将基于使用随机投影开发新的可扩展概率推理方法，以降低概率模型的维数，同时保留其关键属性。所开发的技术将通过开源软件提供给学术界和工业界，并将使计算上可行的方法能够用于分析大型时空数据集和模拟全球尺度现象。该项目产生的预测和数据产品将使新的分析和推进可持续发展学科成为可能。结果将通过科学文章、研究研讨会和会议报告广泛传播，以最大限度地造福科学界。教育和推广工作将包括让本科生参与独立研究项目，建立一个描述桥接计算和研究的网站，以及一个旨在向代表性不足的高中生介绍计算机科学和人工智能的暑期推广项目。

Key sustainability challenges, such as poverty mitigation, climate change, and food security, involve global phenomena that are unique in scale and complexity. Our global sensing capabilities - from remote sensing to crowdsourcing - are becoming increasingly economical and accurate. These recent technological developments are creating new spatio-temporal data streams that contain a wealth of information relevant to sustainable development goals. Actionable insights, however, cannot be easily extracted because the sheer size and unstructured nature of the data preclude traditional analysis techniques. This five-year career-development plan is an integrated research, education, and outreach program focused on developing new AI techniques to extract actionable insights from large-scale spatio-temporal data. These techniques have the potential to yield accurate, inexpensive, and highly scalable models to inform research and policy.The research goal of this project is to develop new modeling and algorithmic frameworks to help address global sustainability challenges involving spatio-temporal data. This research will develop new predictive models of complex spatio-temporal phenomena integrating in unique ways ideas from graphical models and representation learning, improving their overall performance. New approaches to learn from unlabeled data exploiting various forms of prior domain knowledge, including spatio-temporal dependencies and relationships between different data modalities, will be developed. To learn models and make predictions at scale, this project will also develop new scalable probabilistic inference methods based on the use of random projections to reduce the dimensionality of probabilistic models while preserving their key properties. The techniques developed will be made available to both academia and industry through open-source software, and will enable computationally feasible approaches for analyzing large spatio-temporal datasets and for modeling global scale phenomena. Predictions and data products produced by this project will enable new analyses and advance sustainability disciplines. Results will be disseminated widely through scientific articles, research seminars, and conference presentations to maximize the benefits to the scientific community. Educational and outreach efforts will include the involvement of undergraduate students undertaking independent research projects, a website describing research bridging computation and, and a summer outreach program aimed at introducing under-represented high-school students to computer science and artificial intelligence.