Institute for Data, Econometrics, Algorithms and Learning (IDEAL)

数据、计量经济学、算法和学习研究所 (IDEAL)

• 批准号: 2216912
• 负责人: Chao Gao
• 金额: $ 117万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2216912&HistoricalAwards=false
• 关键词: Institute; Data; Econometrics; Algorithms; Learning

The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) will consolidate and amplify research devoted to the foundations of data science across all the major research-focused educational institutions in the greater Chicago area: the University of Illinois at Chicago, Northwestern University, the Toyota Technological Institute at Chicago, the University of Chicago, and the Illinois Institute of Technology. This transdisciplinary institute involves over 50 researchers working on key aspects of the foundations of data science across computer science, electrical engineering, mathematics, statistics, and several related fields like economics, operations research, and law, and they are complemented by members of Google’s learning theory team. Its research goals range from the core foundations of data science to its interfaces with other disciplines: 1) tackling important challenges related to foundations of machine learning and optimization, 2) addressing statistical, algorithmic and mathematical challenges in dealing with high-dimensional data, and 3) exploring the foundations of aspects of data science that interact with society. The institute will foster strong connections with the community and local high schools, broaden participation in data science locally and nationally, and build lasting research and educational infrastructure through its activities. Institute activities will include workshops for undergraduate students, high school teacher workshops, public lectures, and museum exhibit designs. These will build new pathways for undergraduate students, high school students, and the broader public from diverse and underrepresented backgrounds, to increase participation and engagement with scientific fields related to data science.The research thrusts of the institute will center around the foundations of machine learning, high-dimensional data analysis and inference, and data science and society. Specific topics include foundations of deep learning, reinforcement learning, machine learning and logic, network inference, high-dimensional data analysis, trustworthiness & reliability, fairness, and data science with strategic agents. The research activities are designed to facilitate collaboration between the different disciplines and across the five Chicago-area institutions, and they build on the extensive experience from previous efforts of the participating universities. The activities include topical special programs, postdoctoral fellows, co-mentored PhD students, workshops, coordinated graduate courses, visiting fellows, research meetings, and brainstorming sessions. The proposed research will lead to new theoretical frameworks, models, mathematical tools and algorithms for analyzing high-dimensional data, inference and learning. Successful outcomes will also lead to a better understanding of the foundations of data science and machine learning in both strategic and non-strategic environments – including emerging concerns like reliability, fairness, privacy and interpretability as data science interacts with society in various ways. The institute will also have broader impacts of strengthening research and educational infrastructure, developing human resources, broadening participation from underrepresented groups, and by connecting theory to science and industry. The institute will organize activities to engage the community and a diverse group of students at all levels, including introductory workshops for undergraduate research participants, high school student and teacher outreach (through a partnership with the Math Circles of Chicago), and public lectures as part of both our research program and a partnership with the Museum of Science and Industry. The Chicago public institutions that we engage serve a very diverse population, so the outreach, recruitment, and training activities will broaden participation from underrepresented groups. Finally, the institute will have direct engagement with applications and industry through its activities involving Google, other industry partners in the broader Chicago area, and applied data science institutes.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.

数据，计量经济学，算法和学习研究所（理想）将巩固和扩大研究大芝加哥地区所有以研究为重点的教育机构的研究基础：伊利诺伊州芝加哥大学，西北大学芝加哥大学，芝加哥大学，芝加哥大学，芝加哥大学，芝加哥大学，伊利诺伊州技术学院和技术研究所。该跨学科研究所涉及50多名研究人员，研究了跨计算机科学，电气工程，数学，统计数据以及经济学，运营研究和法律等几个相关领域的数据科学基础的关键方面，并且由Google学习理论团队的成员完成。它的研究目标从数据科学的核心基础到与其他学科的界面：1）应对与机器学习和优化基础有关的重要挑战，2）解决统计数据，算法和数学挑战，以处理高维数据，以及3）探索与社会相互作用的方面基础。该研究所将与社区和当地高中建立牢固的联系，扩大本地和全国数据科学的参与，并通过其活动建立持久的研究和教育基础设施。研究所的活动将包括针对本科生，高中教师讲习班，公共讲师和博物馆展览设计的研讨会。这些将为本科生，高中生以及来自潜水员和代表性不足的背景的更广泛的公众建立新的途径，以增加与数据科学相关的科学领域的参与和参与。该研究所的研究将围绕机器学习，高维数据分析以及数据科学和社会的机器学习基础。具体主题包括深度学习，加强学习，机器学习和逻辑，网络推断，高维数据分析，可信赖和可靠性，公平性，公平性以及与战略代理人的数据科学。研究活动旨在促进不同学科与五个芝加哥地区机构之间的合作，并且它们基于参与大学以前的努力的丰富经验。这些活动包括局部特殊课程，博士后研究员，合作的博士生，讲习班，协调的研究生课程，访问研究员，研究会议和集思广益会议。拟议的研究将导致新的理论框架，模型，数学工具和算法，以分析高维数据，推理和学习。成功的结果还将更好地理解战略和非战略环境中数据科学和机器学习的基础，包括随着数据科学以各种方式与社会互动，包括新兴问题，例如可靠性，公平性，隐私性和解释性。该研究所还将在加强研究和教育基础设施，发展人力资源，扩大代表性不足的群体以及将理论与科学和工业联系起来的参与方面产生更广泛的影响。该研究所将组织活动，以吸引社区和各个级别的潜水员群体，包括针对本科研究参与者，高中生和教师宣传的讲习班（通过与芝加哥数学圈的合作关系），以及作为我们研究计划的一部分以及与科学和行业博物馆的合作伙伴关系的一部分。我们参与的芝加哥公共机构为多样化的人口提供服务，因此外展，招聘和培训活动将扩大代表性不足的团体的参与。最后，该研究所将通过其涉及Google，芝加哥地区的其他行业合作伙伴的活动直接与应用程序和行业互动，并应用了数据科学研究所。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准通过评估来评估的。