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

HDR TRIPODS：综合数据科学研究所：理解基本权衡和理论基础的跨学科方法

• 批准号: 1934846
• 负责人: Andrew McGregor
• 金额: $ 150万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934846&HistoricalAwards=false
• 关键词: HDR; TRIPODS; Institute; Integrated; Data

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.

科学、工程和工业的许多领域已经通过采用数据科学的工具和技术而发生了革命性的变化。然而，有必要对现有方法进行严格分析，同时提出新的想法，以便：（a）确保最佳利用现有的计算和统计资源;（B）对相关问题制定有原则的系统方法，而不是依赖于一系列临时解决办法。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。首先是获取相关数据：能否以交互方式收集数据，这是否会降低数据获取的成本？数据是否存在噪声，这会如何影响结果？其次是数据的处理：如果数据不能容纳在单个机器的内存中，我们如何最大限度地减少机器集群内的通信成本？什么时候近似答案就足够了，所需的精度如何与可用的计算资源进行权衡？三是可用数据的预测价值：最终结果的不确定性能否量化？如何有效地评估我们的算法所使用的建模假设？该奖项支持一个数据科学研究所，其主要目标是了解上述问题背后的基本数学和计算问题。最终，这将使从业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专家;应用和计算数学;机器学习和统计;以及编码和信息理论。除上述研究目标外，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)交互式无监督学习问题中查询复杂度最小化。3)理解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)教育计划，包括开发新课程，教授数据科学的基础主题和可在不同机构使用的资源。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NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Model-based identification of conditionally-essential genes from transposon-insertion sequencing data.

• DOI: 10.1371/journal.pcbi.1009273
• 发表时间: 2022-03
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Sarsani V;Aldikacti B;He S;Zeinert R;Chien P;Flaherty P
• 通讯作者: Flaherty P

How Compression and Approximation Affect Efficiency in String Distance Measures

压缩和近似如何影响弦距离测量的效率

• DOI: 10.1137/1.9781611977073.112
• 发表时间: 2022
• 期刊: Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA
• 作者: Ganesh, Arun;Kociumaka, Tomasz;Loncoln, Andrea;and Saha, Barna
• 通讯作者: and Saha, Barna

Graph Reconstruction from Random Subgraphs

从随机子图重建图

• 发表时间: 2022
• 期刊: ICALP 2022
• 作者: McGregor, Andrew;Sengupta, Rik
• 通讯作者: Sengupta, Rik

Reliable Distributed Clustering with Redundant Data Assignment

具有冗余数据分配的可靠分布式集群

• 发表时间: 2020
• 期刊: ISIT
• 作者: Gandikota, Venkata;Mazumdar, Arya;Rawat, Ankit Singh
• 通讯作者: Rawat, Ankit Singh

PredictRoute: A Network Path Prediction Toolkit

• DOI: 10.1145/3543516.3460107
• 发表时间: 2021-05
• 期刊: ACM SIGMETRICS Performance Evaluation Review
• 作者: Rachee Singh;D. Tench;Phillipa Gill;A. Mcgregor