HDR TRIPODS: Penn Institute for Foundations of Data Science

不断增长的数据科学领域有望为社会带来许多好处：个性化的知识和服务；改进的医疗保健；改善个人、组织、国家和国际层面的决策；一个更安全、可能更公平的社会；以及许多其他方面。然而，实现这些承诺的能力关键取决于为该领域建立正确的基本原则。该项目在宾夕法尼亚大学建立了一个名为宾夕法尼亚大学数据科学基础研究所(PIFods)的NSF三脚架研究所，目的是将来自计算机科学、电气工程、统计学和数学等多个学科的科学家和想法聚集在一起，以便共同制定可在未来几十年服务于该领域的长期原则。研究所的主要活动将包括跨学科研究、教育和培训，通过应邀的研讨会和讲习班与更广泛的研究界接触，以及与应用科学家和从业人员接触。PIFods团队试图为以下五个方面制定原则：复杂学习任务的原则；高效优化(凸、非凸和子模块)的原则；流、分布式和大规模并行数据分析的原则；隐私保护和公平保护的数据分析原则；以及可重复数据分析的原则。这些努力中的每一个都解决了数据科学中的一个重要的基本需求。这些需求的范围从设计具有更强性能保证的学习算法和开发自适应环境中的优化原则，到发展对数据科学中各种现代计算资源之间的权衡的基本理解，以及开发保证隐私、公平和可重复性的有意义的概念的数据科学算法。每一次推力都需要几个三脚架学科之间的相互作用；这些推力中的几个也自然地相互作用。在教育和培训方面，PIFods小组已经开办了几个与数据科学有关的新的跨学科课程，旨在开发跨学科的共同语言；在研究所的主持下，该小组将继续进一步开发和完善这些课程，并将纳入这些课程的反馈意见，以便为大学新兴的跨学科数据科学课程提供信息。在应用方面，PIFods小组将积极与应用科学家和数据科学从业人员接触，包括更广泛的大学社区成员和选定的行业从业人员；这些接触将有助于为未来可能出现的更多研究提供信息，并有助于解决社会上由数据驱动的重要问题。该项目是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优势和更广泛的影响审查标准进行评估，被认为值得支持。

The growing field of data science promises to bring many benefits to society: personalized knowledge and services; improved healthcare; improved decision-making at individual, organizational, national, and international levels; a safer and possibly fairer society; and many others. The ability to realize these promises, however, depends critically on building the right foundational principles for the field. This project establishes an NSF TRIPODS Institute, termed the Penn Institute for Foundations of Data Science (PIFODS), at the University of Pennsylvania, with the goal of bringing together scientists and ideas from multiple disciplines, including computer science, electrical engineering, statistics, and mathematics, in order to collectively develop long-lasting principles for data science that can serve the field for decades to come. The main activities of the Institute will include transdisciplinary research, education and training, engagement with the broader research community through invited seminars and workshops, and engagement with applied scientists and practitioners. The PIFODS team seeks to develop principles for the following five thrusts: principles for complex learning tasks; principles for efficient optimization (convex, non-convex, and submodular); principles for streaming, distributed, and massively parallel data analysis; principles for privacy-preserving and fairness-preserving data analysis; and principles for reproducible data analysis. Each of these thrusts addresses an important foundational need in data science. These needs range from designing learning algorithms with stronger performance guarantees, and developing principles for optimization in adaptive settings, to developing a fundamental understanding of the tradeoffs between various modern computational resources in data science, as well as developing data science algorithms that guarantee meaningful notions of privacy, fairness, and reproducibility. Each thrust requires interactions among several of the TRIPODS disciplines; several of these thrusts also naturally interact with each other. On the education and training side, the PIFODS team has already initiated several new transdisciplinary courses related to data science that are aimed at developing a common language across disciplines; under the aegis of the Institute, the team will continue to further develop and refine these courses, and will incorporate feedback from these courses to inform the university's emerging transdisciplinary data science curriculum. On the applications side, the PIFODS team will actively engage with applied scientists and practitioners of data science, including both members of the broader university community and selected industry practitioners; these engagements will both help to inform possible additional research thrusts in the future, and help to solve important data-driven problems in society. 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.