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: 2217023
• 负责人: Lev Reyzin
• 金额: $ 318万
• 依托单位: University of Illinois at 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=2217023&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（IDEAL）将巩固和扩大在大芝加哥地区所有主要以研究为重点的教育机构中致力于数据科学基础的研究：芝加哥伊利诺伊大学、西北大学、芝加哥丰田技术研究所、芝加哥大学和伊利诺伊理工学院。这个跨学科研究所包括50多名研究人员，他们研究数据科学基础的关键方面，涉及计算机科学、电气工程、数学、统计学以及经济学、运筹学和法律等几个相关领域，b谷歌的学习理论团队成员也对他们进行了补充。其研究目标范围从数据科学的核心基础到与其他学科的接口：1)解决与机器学习和优化基础相关的重要挑战，2)解决处理高维数据的统计，算法和数学挑战，以及3)探索与社会互动的数据科学方面的基础。该研究所将促进与社区和当地高中的紧密联系，扩大在当地和全国范围内对数据科学的参与，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为本科生举办的讲习班、高中教师讲习班、公开讲座和博物馆展览设计。这将为来自不同和代表性不足背景的本科生、高中生和更广泛的公众建立新的途径，以增加对与数据科学相关的科学领域的参与和参与。该研究所的研究重点将围绕机器学习、高维数据分析和推理以及数据科学与社会的基础展开。具体主题包括深度学习的基础、强化学习、机器学习与逻辑、网络推理、高维数据分析、可信度与可靠性、公平性，以及策略性代理的数据科学。这些研究活动旨在促进不同学科之间和芝加哥地区五所大学之间的合作，它们建立在参与大学以往努力的丰富经验基础上。活动包括专题特别项目、博士后研究员、共同指导的博士生、研讨会、协调研究生课程、访问学者、研究会议和头脑风暴会议。所提出的研究将导致新的理论框架，模型，数学工具和算法分析高维数据，推理和学习。成功的结果也将使人们更好地理解数据科学和机器学习在战略和非战略环境中的基础——包括随着数据科学以各种方式与社会互动而出现的可靠性、公平性、隐私性和可解释性等问题。该研究所还将产生更广泛的影响，包括加强研究和教育基础设施、开发人力资源、扩大代表性不足群体的参与，以及将理论与科学和工业联系起来。该研究所将组织活动，以吸引社区和不同层次的学生群体，包括本科研究参与者的入门研讨会，高中生和教师的外展（通过与芝加哥数学圈的合作），以及作为我们研究计划和与科学与工业博物馆合作的一部分的公开讲座。我们参与的芝加哥公共机构服务于非常多样化的人群，因此拓展、招聘和培训活动将扩大代表性不足群体的参与。最后，该研究所将通过b谷歌、芝加哥地区其他行业合作伙伴以及应用数据科学研究所的活动，直接参与应用和行业。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Interpreting Training Aspects of Deep-Learned Error-Correcting Codes

解释深度学习纠错码的训练方面

• 发表时间: 2023
• 期刊: International Symposium on Information Theory
• 作者: Devroye, Natasha;Mulgund, Abhijeet;Shekhar, Raj;Turan, Gyorgy;Zefran, Milos;Zhou, Yingyao
• 通讯作者: Zhou, Yingyao