HDR TRIPODS: Collaborative Research: Institute for Data, Econometrics, Algorithms and Learning

数据、计量经济学、算法和学习研究所（IDEAL）是一个多学科（计算机科学、统计学、经济学、电气工程和运筹学）和多机构（西北大学、芝加哥丰田技术研究所和芝加哥大学）合作研究所，专注于数据科学理论基础的关键方面。 该研究所将支持在战略和非战略环境中研究与机器学习、高维数据分析和优化相关的基础问题。 该研究所的主要活动将是以主题为重点的宿舍，将与研讨会和外部访问者协调研究生课程工作。 该研究所将通过一系列举措促进芝加哥地区机构之间的合作，并跨越多个学科。研究议程的几个组成部分有直接的应用领域，PI将涉及发展经济学，在线市场，公共政策以及数据科学家的从业者。 该研究所支持的研究领域集中在三个主要主题：（1）高维数据分析，以解决处理高维数据的算法和统计挑战，并研究诸如度量嵌入，草图和无监督学习问题等主题;（2）战略环境中的数据科学，解决战略行为计量经济模型中的计算和信息理论挑战，如高维结构参数空间的推断，处理未观察到的异质性，部分识别，和计量经济学中的机器学习;（3）机器学习和优化，解决连续和离散优化中的基础问题及其在机器学习中的应用，包括表示学习，学习鲁棒性和非凸优化的可证明边界等主题。 最初，将选择六个研究主题，将各机构的兴趣联系在一起：网络推理和数据科学;深度学习理论;共享数据基础设施的激励;高维统计的鲁棒性;高维数据分析;以及部分识别模型的算法。 将有特殊的宿舍（秋季和春季），研究所将汇集调查人员，博士后和博士。让学生专注于其中一个主题。 在接下来的一个季度（冬季和夏季），团队将继续推进提案主题的研究。该项目是美国国家科学基金会利用数据革命（HDR）大创意活动的一部分。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) is a multi-discipline (computer science, statistics, economics, electrical engineering, and operations research) and multi-institution (Northwestern University, Toyota Technological Institute at Chicago, and University of Chicago) collaborative institute that focuses on key aspects of the theoretical foundations of data science. The institute will support the study of foundational problems related to machine learning, high-dimensional data analysis and optimization in both strategic and non-strategic environments. The primary activity of the institute will be thematically focused quarters which will coordinate graduate course work with workshops and external visitors. The institute will facilitate collaboration between Chicago-area institutions through a number of initiatives, and across multiple disciplines. Several components of the research agenda have direct applications areas, and the PIs will involve practitioners in development economics, online markets, public policy, as well as data scientists. The research areas supported by the institute focus on three broad themes: (1) High dimensional data analysis, to address algorithmic and statistical challenges in dealing with high dimensional data, and investigate topics like metric embeddings, sketching, and problems in unsupervised learning; (2) Data Science in Strategic Environments, to address computational and information theoretic challenges in econometric models of strategic behavior like inference on high-dimensional structural parameter spaces, dealing with unobserved heterogeneity, partial identification, and machine learning in econometrics; and (3) Machine learning and optimization, to address foundational questions in both continuous and discrete optimization and its use in machine learning including topics like representation learning, robustness in learning, and provable bounds for non-convex optimization. Initially, six research topics will be selected that tie interests across the institutions: inference and data science on networks; theory of deep learning; incentives in shared data infrastructure; robustness in high-dimensional statistics; high-dimensional data analysis; and algorithms for partially identified models. There will be special quarters (fall and spring) where the Institute will bring together investigators, postdocs, and Ph.D. students to focus on one of the topics. In the following quarter (winter and summer) teams will continue research that advance the proposal topics.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.