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

可持续发展的关键挑战，如减贫、气候变化和粮食安全，涉及规模和复杂性都独一无二的全球现象。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。这些最新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相关的大量信息。然而，由于数据的巨大规模和非结构化性质排除了传统的分析技术，因此不容易提取可操作的见解。这项为期五年的职业发展计划是一个综合研究、教育和推广计划，专注于开发新的人工智能技术，以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，以帮助应对涉及时空数据的全球可持续发展挑战。本研究将以独特的方式融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。将开发新的方法，利用各种形式的先验领域知识，包括时空相关性和不同数据形态之间的关系，从未标记数据中学习。为了在规模上学习模型和进行预测，该项目还将开发基于随机投影的新的可扩展的概率推理方法，以降低概率模型的维度，同时保留其关键属性。开发的技术将通过开放源码软件向学术界和工业界提供，并将使分析大型时空数据集和模拟全球尺度现象的计算可行的方法成为可能。该项目产生的预测和数据产品将使新的分析成为可能，并推动可持续性学科的发展。结果将通过科学文章、研究研讨会和会议报告广泛传播，以最大限度地造福于科学界。教育和外联工作将包括让进行独立研究项目的本科生参与，一个描述将计算和人工智能联系起来的研究的网站，以及一个旨在向未被充分代表的高中生介绍计算机科学和人工智能的暑期外联计划。

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.