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

职业：大规模时空数据的建模和推理

• 批准号: 1651565
• 负责人: Stefano Ermon
• 金额: $ 54万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651565&HistoricalAwards=false
• 关键词: CAREER; Modeling; Inference; Large; Scale

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.

关键的可持续性挑战，例如缓解贫困，气候变化和粮食安全，涉及规模和复杂性独特的全球现象。从遥感到众包，我们的全球传感能力正在变得越来越经济和准确。这些最新的技术发展正在创建新的时空数据流，其中包含大量与可持续发展目标相关的信息。但是，由于数据的巨大规模和非结构化性质妨碍了传统分析技术，因此无法轻易提取可行的见解。这个五年的职业发展计划是一项综合研究，教育和外展计划，旨在开发新的AI技术，以从大规模时空数据中提取可行的见解。这些技术有可能产生准确，廉价且高度可扩展的模型来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。这项研究将开发出复杂时空现象的新预测模型，以独特的方式整合图形模型和表示学习，从而提高其整体绩效。将开发新的方法来从无标记的数据中学习利用各种形式的先前领域知识，包括时空依赖性以及不同数据模式之间的关系。为了学习模型并进行大规模预测，该项目还将根据使用随机投影来降低概率模型的维度，同时保留其关键属性，从而开发新的可扩展概率推理方法。开发的技术将通过开源软件提供给学术界和行业，并将启用计算可行的方法，用于分析大型时空数据集和对全球规模现象进行建模。该项目生产的预测和数据产品将实现新的分析并提高可持续性学科。结果将通过科学文章，研究研讨会和会议演讲来广泛传播，以最大程度地利用科学界的好处。教育和推广工作将包括从事独立研究项目的本科生的参与，一个描述研究桥接计算的网站以及旨在将代表性不足的高中生引入计算机科学和人工智能的夏季推广计划。

项目成果

ButterflyFlow: Building Invertible Layers with Butterfly Matrices

• DOI: 10.48550/arxiv.2209.13774
• 发表时间: 2022-09
• 期刊: ArXiv
• 作者: Chenlin Meng;Linqi Zhou;Kristy Choi;Tri Dao;Stefano Ermon

Using satellite imagery to understand and promote sustainable development

• DOI: 10.1126/science.abe8628
• 发表时间: 2021-03-19
• 期刊: SCIENCE
• 影响因子: 56.9
• 作者: Burke, Marshall;Driscoll, Anne;Ermon, Stefano
• 通讯作者: Ermon, Stefano

Geography-Aware Self-Supervised Learning

• DOI: 10.1109/iccv48922.2021.01002
• 发表时间: 2021-01-01
• 期刊: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
• 作者: Ayush, Kumar;Uzkent, Burak;Ermon, Stefano
• 通讯作者: Ermon, Stefano

Generative Modeling by Estimating Gradients of the Data Distribution

• 发表时间: 2019-07
• 作者: Yang Song;Stefano Ermon

Negative Data Augmentation

• 发表时间: 2021-02
• 期刊: ArXiv
• 作者: Abhishek Sinha;Kumar Ayush;Jiaming Song;Burak Uzkent;Hongxia Jin;Stefano Ermon