HDR TRIPODS: Institute for Integrated Data Science: A Transdisciplinary Approach to Understanding Fundamental Trade-offs and Theoretical Foundations

通过采用数据科学的工具和技术，科学、工程和工业的许多领域已经发生了革命性的变化。然而，有必要对现有方法进行严格的分析，并提出新的想法，以确保对现有计算和统计资源的最佳利用，以及b)对相关问题制定有原则和系统的方法，而不是依赖于一系列临时解决方案。特别是，在典型的数据科学项目中会出现许多相互关联的问题。首先是相关数据的获取：是否可以交互式地收集数据，这是否可以降低数据获取的成本？数据是否有噪声，这将如何影响结果？其次是数据的处理：如果数据无法在单个机器的内存中存储，我们如何最小化机器集群中的通信成本？什么时候近似答案是足够的，所需的准确性如何与可用的计算资源相权衡？三是可用数据的预测值：最终结果的不确定性能否量化？我们的算法所使用的建模假设如何有效地被评估？该奖项支持一个数据科学研究所，其主要目标是发展对上述问题背后的基本数学和计算问题的理解。最终，这将使从业者在数据科学项目的整个生命周期中投入时间和金钱时做出更明智的决策。实现这一目标需要跨学科的方法，研究团队包括理论计算机科学的专家；应用与计算数学；机器学习与统计学；以及编码和信息论。除了追求上述研究目标外，研究所还将协调教育和培训活动，并为研究界开发资源。本项目探讨的具体研究目标包括：1)了解交互式数据采集与统计和计算效率之间的权衡。2)最小化交互式无监督学习问题的查询复杂度。3)在处理随机数据时理解空间/样本复杂性的权衡。4)开发与核心数据科学任务相关的细粒度近似算法。5)利用编码理论实现高效通信的分布式机器学习。6)在资源有限的情况下，设计具有统计保证的变分推理方法。7)开发一种有原则的方法来利用偏差、模型复杂性和计算预算之间的权衡。具体的研究所活动包括：1)领域科学研究人员的技术讲习班和培训活动。2)虚拟演讲者系列。3)教育计划，包括开发新课程，教授数据科学的基础主题和可供不同机构使用的资源。该基金还将培养博士后学者和本科生研究人员。该项目是美国国家科学基金会“利用数据革命（HDR）大创意”活动的一部分。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many areas of science, engineering, and industry are already being revolutionized by the adoption of tools and techniques from data science. However, a rigorous analysis of existing approaches together with the development of new ideas is necessary to a) ensure the optimal use of available computational and statistical resources and b) develop a principled and systematic approach to the relevant problems rather than relying on a collection of ad hoc solutions. In particular, there are many interrelated questions that arise in a typical data science project. First is the acquisition of relevant data: Can data be collected interactively and might this reduce the costs of data acquisition? Is the data noisy and how might this impact the results? Second is the processing of data: If the data cannot fit in the memory of a single machine, how can we minimize the communication costs within a cluster of machines? When are approximate answers sufficient and how does the required accuracy trade off with the computational resources available? Third is the prediction value of the available data: Can the uncertainty of the final results be quantified? How can the modeling assumptions used by our algorithms be efficiently evaluated? This award supports a data science institute with the main goal of developing an understanding of the fundamental mathematical and computational issues underlying the aforementioned questions. Ultimately, this will enable practitioners to make more informed decisions when investing time and money across the life cycle of their data science project. Achieving this goal necessitates a transdisciplinary approach and the team of investigators includes experts in theoretical computer science; applied and computational mathematics; machine learning and statistics; and coding and information theory. In addition to pursuing the above research goals, the institute will coordinate education and training activities and develop resources for the research community.Specific research goals explored in this project include: 1) Understanding the trade-off between rounds of interactive data acquisition and statistical and computational efficiency. 2) Minimizing query complexity in interactive unsupervised learning problems. 3) Understanding space/sample complexity trade-offs when processing stochastic data. 4) Developing fine-grained approximation algorithms relevant to core data science tasks. 5) Using coding theory to enable communication-efficient distributed machine learning. 6) Designing variational inference methods with statistical guarantees given limited resources. 7) Developing a principled approach to exploiting trade-offs between bias, model complexity, and computational budget. Specific institute activities include: 1) Technical workshops and training activities for researchers in domain sciences. 2) A virtual speaker series. 3) Education initiatives including the development of new courses that will teach foundational topics in data science and resources that can be used across different institutions. The grant will also train postdoctoral scholars and undergraduate researchers.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.