TRIPODS: From Foundations to Practice of Data Science and Back

近几十年来，科学和技术领域经历了“数据时刻”，因为研究人员认识到通过将计算统计和机器学习技术应用于不断增长的数据集来得出新型推论的潜力。与此同时，日常生活中越来越多地充斥着数据分析产品：搜索引擎、推荐系统、自动驾驶汽车等，这些发展提出了基本的方法论问题，包括如何收集和预帕雷数据以供分析，以及如何将统计推断转化为有效的行动和新的统计调查。为了解决这些问题，有必要为数据科学的实践奠定理论基础，并为从业人员提供合理和实际相关的方法论培训。哥伦比亚TRIPODS研究所通过数据科学基础、课程开发和中心建设活动的综合研究计划来实现这些目标。该研究计划旨在提供对实际分析的理论理解，开发数据科学计算原语的模块化和结构良好的工具包，并支持整个数据科学周期，从数据收集和注释到分析产品的评估。该研究所致力于研究，教育和中心建设计划，旨在阐明数据科学的理论基础。其活动旨在对塑造这一新兴领域产生重大影响。研究方向包括理解易于处理的优化问题，开发支持数据高效计算的原语，以及为数据科学中的交互式协议开发方法论基础。这些方向解决了理论与实践之间的接口具有挑战性的问题，其解决方案需要跨越数学，统计和计算的思想。教育活动阐明了MS/专业和博士水平的数据科学示范课程，包括旨在为新一代科学家和工程师建立共同语言的跨学科课程。中心建设活动围绕跨学科主题组织，其结构旨在鼓励跨学科互动，并在数据科学基础中开发一个共同的方法论社区。这些研究和教育活动，包括研讨会，暑期学校，杰出的系列讲座，长期访问和推广，有助于进一步定义和传播基础研究和教育的共同语言，并增加数据科学的多元化参与。该研究所位于哥伦比亚大学数据科学研究所的数据科学基础中心内，是东北大数据中心的中心。该职位支持在哥伦比亚以及其他大数据中心成员、TRIPODS研究所和研究/行业组织内扩展活动。 该项目的资金来自CISE计算和通信基金会，CISE信息技术研究，MPS数学科学部和MPS多学科活动办公室。

In recent decades, scientific and technological fields have experienced "data moments" as researchers recognized the potential of drawing new types of inferences by applying techniques from computational statistics and machine learning to ever-growing datasets. At the same time, everyday life is increasingly saturated with products of data analysis: search engines, recommendation systems, autonomous vehicles, etc. These developments raise fundamental methodological questions, including how to collect and pre- pare data for analysis, and how to transform statistical inferences into effective action and new statistical inquiries. To address these questions, it is necessary to develop theoretical foundations for the practice of data science, and to provide practitioners with sound and practically relevant methodological training. The Columbia TRIPODS Institute pursues these goals through an integrated program of research in data science foundations, curriculum development, and center-building activities. The research program seeks to provide theoretical understanding of practical heuristics, develop modular and well-structured toolkits of computational primitives for data science, and to support the entirety of the data science cycle, from data collection and annotation, to the assessment of the analysis product. The Institute pursues programs of research, education and center-building aimed at articulating theoretical foundations for data science. Its activities aim to have a major impact in shaping this emerging field. The research directions include understanding tractable classes of optimization problems, developing primitives that support efficient computation on data, and developing methodological foundations for interactive protocols in data science. These directions address challenging problems at the interface between theory and practice, the solutions of which require ideas spanning mathematics, statistics, and computing. The educational activities articulate model curricula in data science at the MS/professional and PhD levels, including interdisciplinary courses aimed at building a common language for a new generation of scientists and engineers. Center building activities are organized around cross-disciplinary themes and structured to encourage interaction across disciplines and to develop a common methodological community in Foundations of Data Science. These research and educational activities-including workshops, summer schools, distinguished lecture series, long-term visits, and outreach- help to further define and disseminate a common language for foundational research and education, and to increase diverse participation in data science. Located within the Center for Foundations of Data Science in the Data Science Institute at Columbia University, the Institute is at the center of the Northeast Big Data Hub. This position supports expansion of activities within Columbia, and also with other Big Data Hub members, TRIPODS Institutes, and research/industry organizations. Funds for the project come from CISE Computing and Communications Foundations, CISE Information Technology Research, MPS Division of Mathematical Sciences, and MPS Office of Multidisciplinary Activities.