TRIPODS: Berkeley Institute on the Foundations of Data Analysis

为了响应NSF的TRIPODS第一阶段计划，在加州大学伯克利分校拥有理论和应用统计学、计算机科学和数学专业知识的PI将创建一个数据分析基础（FODA）研究所，以解决跨学科数据科学中的前沿基础问题。 该研究所将推进基础研究和基础方法的应用，通过一个密集的跨学科推广计划，以应用领域内外的校园研究社区。 在过去十年中，随着基础学科的技术和方法的巨大进步，一系列与数据相关的研究和培训项目在校园内蓬勃发展。 然而，校园数据科学生态系统中的这些项目都没有致力于以集中的、任务驱动的方式解决数据分析的跨学科基础。 FODA研究所将解决这一关键的未满足的需求。 这个跨学科的项目将为理论倾向的数据科学研究人员和不同领域的研究人员之间更富有成效和成果的互动奠定基础，这些领域依赖于但并不总是明确地理解基础概念。 这一领域的进步将导致从广泛领域的数据中更有原则地提取见解。 为期三年的第一阶段试点将为该项目的制度化铺平道路，作为一个更大的中心，这将是一个潜在的第二阶段应用的主题。该项目的技术研究部分解决了数据科学中的四个基本挑战：从推理优化问题的上限和下限的角度来描述什么是可能的，什么是不可能的;更深入地探索作为计算推理原则的稳定性概念;探索随机性作为统计资源、算法资源和数据驱动计算数学工具的补充作用;并开发方法，以原则性的方式将基于联合收割机的科学模型与数据驱动模型相结合。 这些挑战中的每一个都根据新的需求解决了旧的问题，每一个都与其他挑战有重要的协同作用，每一个都正好位于理论计算机科学，理论统计学和应用数学的界面。 该项目将弥合潜在的跨学科差距，以解决当今数据科学核心的一些最重要的问题。 该项目的资金来自CISE计算和通信基金会以及MPS数学科学部。

In response to NSF's TRIPODS Phase I initiative, the PIs, with expertise in theoretical and applied statistics, computer science, and mathematics at the University of California, Berkeley, will create a Foundations of Data Analysis (FODA) Institute to address cutting-edge foundational issues in interdisciplinary data science. The Institute will advance foundational research and the application of foundational methods through an intensive program of cross-disciplinary outreach to application domains in and beyond the campus research community. In parallel with the massive technological and methodological advances in the underlying disciplines over the past decade, a thriving array of data-related research and training programs has emerged across campus. Yet none of these programs within the campus data science ecosystem are devoted to addressing the interdisciplinary foundations of data analysis in a focused, mission-driven manner. The FODA Institute will address this crucial unmet need. This interdisciplinary project will lay the groundwork for more productive and fruitful interactions between theoretically-inclined data science researchers and researchers in diverse domains that rely upon, but do not always explicitly appreciate, foundational concepts. Advances in this area will lead to more principled extraction of insights from data across a wide range of domains. The three-year Phase I pilot will pave the way for institutionalization of the project as a larger center that will be the subject of a potential Phase II application.The technical research component of the project addresses four fundamental challenges in data science: the characterization of what is, and what is not, possible in terms of upper and lower bounds for inferential optimization problems; probing more deeply the notion of stability as a computational-inferential principle; exploring the complementary role of randomness as a statistical resource, as an algorithmic resource, and as a tool for data-driven computational mathematics; and developing methods to combine science-based with data-driven models in a principled manner. Each of these challenges addresses old questions in light of new needs, each has important synergies with the other challenges, and each is situated squarely at the interface of theoretical computer science, theoretical statistics, and applied mathematics. The project will bridge the underlying interdisciplinary gaps to address some of the most important questions at the heart of data science today. Funds for the project come from CISE Computing and Communications Foundations and MPS Division of Mathematical Sciences.