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Foundations of Data Science Institute

数据科学研究所基础

基本信息

批准号：
2022205
负责人：
Paul Hand
金额：
$ 34.24万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2022205&HistoricalAwards=false
关键词：
Foundations Data Science Institute

项目摘要

The Foundations of Data Science Institute (FODSI) brings together a large and diverse team of researchers and educators from UC Berkeley, MIT, Boston University, Bryn Mawr College, Harvard University, Howard University, and Northeastern University, with the aim of advancing the theoretical foundations for the field of data science. Data science has emerged as a central science for the 21st century, a widespread approach to science and technology that exploits the explosion in the availability of data to allow empirical investigations at unprecedented scale and scope. It now plays a central role in diverse domains across all of science, commerce and industry. The development of theoretical foundations for principled approaches to data science is particularly challenging because it requires progress across the full breadth of scientific issues that arise in the rich and complex processes by which data can be used to make decisions. These issues include the specification of the goals of data analysis, the development of models that aim to capture the way in which data may have arisen, the crafting of algorithms that are responsive to the models and goals, an understanding of the impact of misspecifications of these models and goals, an understanding of the effects of interactions, interventions and feedback mechanisms that affect the data and the interpretation of the results, concern about the uncertainty of these results, an understanding of the impact of other decision-makers with competing goals, and concern about the economic, social, and ethical implications of automated data analysis and decision-making. To address these challenges, FODSI brings together experts from many cognate academic disciplines, including computer science, statistics, mathematics, electrical engineering, and economics. Institute research outcomes have strong potential to directly impact the many application domains for data science in industry, commerce, science and society, facilitated by mechanisms that directly involve a stream of institute-trained personnel in industrial partners' projects, and by public activities designed to nurture substantive interactions between foundational and use-inspired research communities in data science. The institute also aims to educate and mentor future leaders in data science, through the further development of a pioneering undergraduate program in data science, and by training a diverse cohort of graduate students and postdocs with an innovative approach that emphasizes strong mentorship, flexibility, and breadth of collaboration opportunities. In addition, the institute plans to host an annual summer school that will deliver core curriculum and a taste of foundational research to a diverse group of advanced undergraduates, graduate students, and postdocs. It aims to broaden participation and increase diversity in the data science workforce, bringing the excitement of data science to under-represented groups at the high school level, and targeting diverse participation in the institute's public activities. And it will act as a nexus for research and education in the foundations of data science, by convening public events, such as summer schools and research workshops and other collaborative research opportunities, and by providing models for education, human resource development, and broadening participation. The scientific focus of the institute will encompass the full range of issues that arise in data science -- modeling issues, inferential issues, computational issues, and societal issues – and the challenges that emerge from the conflicts between their competing requirements. Its research agenda is organized around eight themes. Three of these themes focus on key challenges arising from the rich variety of interactions between a decision maker and its environment, not only the classical view of data that is processed in a batch or a stream, but also sequential interactions with feedback (the control perspective), experimental interactions designed to answer "what if" questions (the causality perspective), and strategic interactions involving other actors with conflicting goals (the economic perspective). The other research themes focus on opportunities for major impacts across disciplinary boundaries: on elucidating the algorithmic landscape of statistical problems, and in particular the computational complexity of statistical estimation problems, on sketching, sampling, and sub-linear time algorithms designed to address issues of scalability in data science problems; on exploiting statistical methodology in the service of algorithms; and on using breakthroughs in applied mathematics to address computational and inferential challenges. Intellectual contributions to societal issues in data science will feature throughout this set of themes. The institute will exploit strong connections with its scientific and industrial partners to ensure that these research directions enjoy a rich engagement with a broad range of commercial, technological and scientific application domains. Its sequence of research workshops and a collaborative research program will serve the broader research community by nurturing additional research in these key challenge areas. The institute will be led by a steering committee that will seek the help of an external advisory board to prioritize its research themes and activities throughout its lifetime. Its educational programs will include curriculum development from K-12 through undergraduate, a graduate level visit program, and a postdoc training model, aimed at empowering the next generation of leaders to fluidly work across conventional disciplinary boundaries while being mindful of the full set of scientific issues. The institute will undertake a multi-pronged effort to recruit, engage and support the full range of groups traditionally under-represented in mathematics, computer science and statistics.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.

数据科学研究所基金会 (FODSI) 汇集了来自加州大学伯克利分校、麻省理工学院、波士顿大学、布林莫尔学院、哈佛大学、霍华德大学和东北大学的庞大且多元化的研究人员和教育工作者团队，旨在推进数据科学领域的理论基础。数据科学已成为 21 世纪的核心科学，是一种广泛的科学技术方法，它利用数据可用性的爆炸式增长，以前所未有的规模和范围进行实证研究。它现在在科学、商业和工业的各个领域发挥着核心作用。数据科学原则方法的理论基础的发展尤其具有挑战性，因为它需要在利用数据做出决策的丰富而复杂的过程中出现的所有科学问题上取得进展。这些问题包括数据分析目标的规范，旨在捕获数据可能出现方式的模型的开发，响应模型和目标的算法的设计，对这些模型和目标的错误指定的影响的理解，对影响数据和结果解释的交互、干预和反馈机制的影响的理解，对这些结果的不确定性的关注，对其他因素的影响的理解决策者具有相互竞争的目标，并关注自动化数据分析和决策的经济、社会和道德影响。为了应对这些挑战，FODSI 汇集了来自许多相关学科的专家，包括计算机科学、统计学、数学、电气工程和经济学。研究所的研究成果具有强大的潜力，可以直接影响工业、商业、科学和社会中数据科学的许多应用领域，这得益于直接让研究所培训的人员参与工业合作伙伴项目的机制，以及旨在培养数据科学领域的基础和使用启发的研究社区之间实质性互动的公共活动。该研究所还旨在通过进一步发展数据科学领域的开创性本科课程，并通过强调强有力的指导、灵活性和广泛的合作机会的创新方法来培训多元化的研究生和博士后群体，来教育和指导数据科学领域的未来领导者。此外，该研究所还计划举办一年一度的暑期学校，为各类高年级本科生、研究生和博士后提供核心课程和基础研究体验。它旨在扩大数据科学劳动力的参与度并增加其多样性，为高中阶段代表性不足的群体带来数据科学的乐趣，并瞄准研究所公共活动的多元化参与。它将通过举办暑期学校、研究研讨会和其他合作研究机会等公共活动，并提供教育、人力资源开发和扩大参与的模式，成为数据科学基础研究和教育的纽带。该研究所的科学重点将涵盖数据科学中出现的全方位问题——建模问题、推理问题、计算问题和社会问题——以及相互竞争的需求之间的冲突所带来的挑战。其研究议程围绕八个主题组织。其中三个主题侧重于决策者与其环境之间丰富多样的交互所带来的关键挑战，不仅是批处理或流处理的数据的经典视图，还包括与反馈的顺序交互（控制视角）、旨在回答“假设”问题的实验交互（因果关系视角）以及涉及目标相互冲突的其他参与者的战略交互（经济视角）。其他研究主题侧重于跨学科边界产生重大影响的机会：阐明统计问题的算法前景，特别是统计估计问题的计算复杂性，旨在解决数据科学问题中的可扩展性问题的草图、采样和次线性时间算法；利用统计方法为算法服务；以及利用应用数学的突破来解决计算和推理挑战。数据科学中对社会问题的智力贡献将贯穿这组主题。该研究所将利用与科学和工业合作伙伴的紧密联系，确保这些研究方向与广泛的商业、技术和科学应用领域有丰富的接触。其一系列研究研讨会和合作研究计划将通过培育这些关键挑战领域的更多研究来服务更广泛的研究界。该研究所将由一个指导委员会领导，该委员会将寻求外部顾问委员会的帮助，以确定其整个生命周期中研究主题和活动的优先顺序。其教育计划将包括从K-12到本科生的课程开发、研究生水平访问计划和博士后培训模式，旨在使下一代领导者能够跨传统学科界限流畅地工作，同时关注全套科学问题。该研究所将采取多管齐下的努力，招募、参与和支持传统上在数学、计算机科学和统计学领域代表性不足的各个群体。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。