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，有必要对现有办法进行严格分析，同时提出新的想法，以a)确保最佳利用现有的计算和统计资源，b)为相关问题制定一种原则性和系统性的办法，而不是依赖于一系列特别的解决办法。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：数据能否以交互方式收集，这是否会降低数据获取的成本？数据是否有噪音？这可能会对结果产生怎样的影响？第二是数据的处理：如果一台机器的内存无法容纳数据，我们如何才能最大限度地减少机器集群内的通信成本？什么时候近似答案是足够的，所需的精度与可用的计算资源是如何权衡的？三是现有数据的预测值：最终结果的不确定性能否量化？我们的算法使用的建模假设如何才能被有效地评估？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，这将使实践者在其数据科学项目的整个生命周期中投入时间和金钱时，能够做出更明智的决定。要实现这一目标，就必须采取跨学科的方法，调查小组包括理论计算机科学、应用数学和计算数学、机器学习和统计学以及编码和信息理论方面的专家。除了追求上述研究目标外，该研究所还将协调教育和培训活动，并为研究界开发资源。本项目探索的具体研究目标包括：1)了解几轮交互数据获取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中的查询复杂度。3)了解在处理随机数据时的空间/样本复杂性权衡。4)开发与核心数据科学任务相关的细粒度近似算法。5)使用编码理论实现通信高效的分布式机器学习。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)开发一种原则性的方法来利用偏差、模型复杂性和计算预算之间的权衡。研究所的具体活动包括：1)为领域科学研究人员举办技术讲习班和培训活动。2)虚拟扬声器系列。3)教育举措，包括开发新的课程，教授数据科学的基础主题和可供不同机构使用的资源。这笔奖金还将培训博士后学者和本科生研究人员。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。

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.