TRIPODS: Institute for Foundations of Data Science

数据科学正在对科学和社会产生巨大影响，但它的成功也揭示了阻碍进一步进步的紧迫新挑战。许多机器学习过程产生的结果和决策对数据中的错误和损坏并不健壮；随着对数据隐私的担忧继续增加，数据科学算法正在产生有偏见和不公平的结果；适合动态、交互环境的机器学习系统不如解决静态问题的相应工具开发得更好。只有通过呼吁数据科学的基础，我们才能理解和应对这样的挑战。在三个三脚架第一阶段研究所工作的基础上，新的数据科学基础研究所(IFDS)汇集了来自华盛顿大学、威斯康星麦迪逊大学、加利福尼亚州圣克鲁斯大学和芝加哥大学的研究人员，围绕解决这些关键问题的目标进行了组织。IFDS的成员在数学、统计学和理论计算机科学三足鼎立的学科领域具有互补的优势，并通过综合不同领域的知识和经验来推动理论边界的成熟合作记录。IFDS的学生和博士后成员将接受培训，以流利地掌握几个学科的语言，并能够在这些社区之间架起桥梁，在数据科学基础上进行跨学科研究。与其研究议程相一致，IFDS将通过研讨会、暑期学校和黑客松吸引数据科学界的参与。它的多样化领导层致力于公平和包容，提出了广泛的计划，以接触传统上代表性不足的群体。研究所的治理、管理和评估将建立在第一阶段开发的成功和高效模式的基础上。为了解决数据科学研究前沿的关键问题，IFDS将围绕四个核心主题组织其研究。复杂性主题将综合来自多个学科的各种复杂性概念，在优化和抽样方法的分析方面取得突破，开发评估数据模型复杂性的工具，并寻求具有更好复杂性属性的新方法，使复杂性成为理解和发明数据科学算法的更强大工具。稳健性主题考虑包含错误或离群值的数据，可能是由于对手，并将设计在面对这些错误时具有健壮性的数据分析和预测方法。关于闭环数据科学的主题解决了以有效揭示数据的信息内容的方式获取数据的问题，使用了利用已经从过去数据中收集的信息的战略和顺序政策。关于伦理和算法的主题解决了机器学习中的公平和偏见、数据隐私以及因果关系和可解释性的问题。这四个主题在许多方面相交，大多数IFDS研究人员将在其中两个或更多个主题中开展工作。通过在这些基本方面取得协调一致的进展，IFDS将降低更好地理解数据科学方法、提高其有效性和与应用领域更广泛相关的几个障碍。此外，IFDS将组织和主办各种活动，让各级数据科学界参与进来。年度讲习班将集中讨论上述确定的关键问题以及今后五年肯定会出现的其他问题。推广和教育的全面计划将借鉴第一阶段研究所以前的经验，并利用四个地点的机构资源。与学术界、国家实验室和行业的领域科学研究人员的合作，对于阐明数据科学基础中的问题是如此重要，将通过IFDS成员可用的许多渠道继续进行，包括那些在Tripods X计划中建立的渠道。与每个IFDS站点的其他研究所的关系将进一步扩大IFDS对领域科学和应用的影响。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。

Data science is making an enormous impact on science and society, but its success is uncovering pressing new challenges that stand in the way of further progress. Outcomes and decisions arising from many machine learning processes are not robust to errors and corruption in the data; data science algorithms are yielding biased and unfair outcomes, as concerns about data privacy continue to mount; and machine learning systems suited to dynamic, interactive environments are less well developed than corresponding tools for static problems. Only by an appeal to the foundations of data science can we understand and address challenges such as these. Building on the work of three TRIPODS Phase I institutes, the new Institute for Foundations of Data Science (IFDS) brings together researchers from the Universities of Washington, Wisconsin-Madison, California-Santa Cruz, and Chicago, organized around the goal of tackling these critical issues. Members of IFDS have complementary strengths in the TRIPODS disciplines of mathematics, statistics, and theoretical computer science, and a proven record of collaborating to push theoretical boundaries by synthesizing knowledge and experience from diverse areas. Students and postdoctoral members of IFDS will be trained to be fluent in the languages of several disciplines, and able to bridge these communities and perform transdisciplinary research in the foundations of data science. In concert with its research agenda, IFDS will engage the data science community through workshops, summer schools, and hackathons. Its diverse leadership, committed to equity and inclusion, proposes extensive plans for outreach to traditionally underrepresented groups. Governance, management, and evaluation of the institute will build on the successful and efficient models developed during Phase I.To address critical issues at the cutting edge of data science research, IFDS will organize its research around four core themes. The complexity theme will synthesize various notions of complexity from multiple disciplines to make breakthroughs in the analysis of optimization and sampling methods, develop tools for assessing the complexity of data models, and seek new methods with better complexity properties, to make complexity a more powerful tool for understanding and inventing algorithms in data science. The robustness theme considers data that contains errors or outliers, possibly due to an adversary, and will design methods for data analysis and prediction that are robust in the face of these errors. The theme on closed-loop data science tackles the issues of acquiring data in ways that reveal the information content of the data efficiently, using strategic and sequential policies that leverage information gathered already from past data. The theme on ethics and algorithms addresses issues of fairness and bias in machine learning, data privacy, and causality and interpretability. The four themes intersect in many ways, and most IFDS researchers will work in two or more of them. By making concerted progress on these fundamental fronts, IFDS will lower several of the barriers to better understanding of data science methodology and to its improved effectiveness and wider relevance to application areas. Additionally, IFDS will organize and host activities that engage the data science community at all levels of seniority. Annual workshops will focus on the critical issues identified above and others that are sure to arise over the next five years. Comprehensive plans for outreach and education will draw on previous experience of the Phase I institutes and leverage institutional resources at the four sites. Collaborations with domain science researchers in academia, national laboratories, and industry, so important in illuminating issues in the fundamentals of data science, will continue through the many channels available to IFDS members, including those established in the TRIPODS+X program. Relationships with other institutes at each IFDS site will further extend the impact of IFDS on domain sciences and applications.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.