TRIPODS: UA-TRIPODS - Building Theoretical Foundations for Data Sciences

TRIPODS：UA-TRIPODS - 为数据科学奠定理论基础

• 批准号: 1740858
• 负责人: Hao Zhang
• 金额: $ 136.85万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740858&HistoricalAwards=false
• 关键词: TRIPODS; UA; Building; Theoretical; Foundations

UA-TRIPODS aims to build a transformative and highly interactive data science hub in the Southwestern United States for the integration of research, education, and outreach in the theoretical foundations of data science. Through research, education, and outreach targeted to high school students through postdoctoral fellows, UA-TRIPODS will help establish and encourage a diverse population to pursue careers in quantitative and computational data science. The intellectual merits of the project include identifying fundamental areas where progress in the theoretical foundations is imperative, developing mathematical and algorithmic principles of data science, and basic research on topics ranging from the theory of large scale networks and optimization to statistical modeling for natural language processing and Bayesian methods. This is accomplished by extending long-standing collaborations with experts in the foundational disciplines - theoretical computer science, mathematics, and statistics - and motivated by problems in the domain sciences and industry. New mathematical, statistical, and algorithmic principles will strengthen the foundations of theoretical data science, while driving discovery and innovation for emerging applications. UA-TRIPODS will also cultivate new partnerships with local industries engaged in data-centric enterprises in astronomy, environmental science, genomics, lunar and planetary sciences, medicine, transportation, and optical sciences.Research Working Groups will pursue fundamental questions in a number of different areas in the theoretical foundations of data science. By ensuring each group includes researchers from each area of the foundational disciplines, while also pairing these groups with science and industry partners, UA-TRIPODS will ensure that these projects will benefit from a truly transdisciplinary collaboration. Progress in collaboration will be measured by specialized program evaluation metrics. Yearly workshops, seminars, and brainstorming/visioning activities will enable UA-TRIPODS to lay the foundation for the future of training and research in data science. In addition, this project will support the development of a new undergraduate degree in Statistics and Data Science at the University of Arizona, expanded online offerings, and facilitation of cooperation in graduate education (both among the foundational disciplines and with the domain sciences). Students will be provided with opportunities to experience first-hand data analysis and interdisciplinary research and education. UA-TRIPODS will also engage partnering colleges and universities across the Southwest in the institute activities and then utilize these connections to support novel and effective inter-university and intra-university research and educational programs. Partnerships with Hispanic Serving Institutions and Tribal Colleges in southern California, Arizona, New Mexico, and western Texas will be strengthened.

UA-Tripods旨在在美国西南部建立一个变革性和高度互动的数据科学枢纽，以在数据科学的理论基础中整合研究，教育和外展。 通过通过博士后研究员针对高中生的研究，教育和外展，UA-Tripods将有助于建立和鼓励多样化的人群从事定量和计算数据科学的职业。 该项目的智力优点包括确定理论基础中进步的基本领域，必须发展数据科学的数学和算法原理，以及有关主题的基础研究，从大规模网络理论以及优化到自然语言处理的统计模型和贝叶斯方法的统计模型。 这是通过与基础学科的专家（理论计算机科学，数学和统计数据）进行长期合作来实现的，并由领域科学和行业的问题进行。 新的数学，统计和算法原则将加强理论数据科学的基础，同时推动新兴应用的发现和创新。 UA-Tripods还将与从事以数据为中心的企业从事天文学，环境科学，基因组学，月球和行星科学，医学，运输和光学科学的本地行业建立新的伙伴关系。研究工作组将在理论基础的许多不同领域中追求数据科学的许多不同领域的基本问题。 通过确保每个小组都包括来自基础学科每个领域的研究人员，同时还将这些小组与科学和行业合作伙伴配对，UA-Tripods将确保这些项目将从真正的跨学科合作中受益。 协作的进度将通过专门的计划评估指标来衡量。 年度研讨会，研讨会和集思广益/远见活动将使UA-Tripods能够为数据科学培训和研究的未来奠定基础。 此外，该项目将支持亚利桑那大学统计和数据科学的新本科学位，扩大在线产品以及促进研究生教育合作的促进（包括基础学科和领域科学）。 将为学生提供体验第一手数据分析和跨学科研究和教育的机会。 UA-Tripods还将与西南地区的合作学院和大学进行研究所活动，然后利用这些联系来支持新颖有效的大学间和大学内研究和教育计划。 与南加州，亚利桑那州，新墨西哥州和德克萨斯州西部的西班牙裔服务机构和部落学院的合作伙伴关系将得到加强。

项目成果

Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data

• DOI: 10.1029/2020wr027948
• 发表时间: 2021-06-01
• 期刊: WATER RESOURCES RESEARCH
• 影响因子: 5.4
• 作者: Gharari, Shervan;Gupta, Hoshin V.;Savenije, Hubert H. G.
• 通讯作者: Savenije, Hubert H. G.

Rearranged Fourier Series and Generalizations to Non-Commutative Groups

重新排列傅立叶级数和对非交换群的推广

• DOI: 10.1109/sampta45681.2019.9030904
• 发表时间: 2020
• 期刊: 2019 13th International conference on Sampling Theory and Applications (SampTA
• 作者: Hamm, Keaton;Hayes, Benjamin;Petrosyan, Armenak
• 通讯作者: Petrosyan, Armenak

Graph spanners: A tutorial review

图形扳手：教程回顾

• DOI: 10.1016/j.cosrev.2020.100253
• 发表时间: 2020
• 期刊: Computer Science Review
• 影响因子: 12.9
• 作者: Ahmed, Reyan;Bodwin, Greg;Sahneh, Faryad Darabi;Hamm, Keaton;Latifi Jebelli, Mohammad Javad;Kobourov, Stephen;Spence, Richard
• 通讯作者: Spence, Richard

Intra-hour cloud index forecasting with data assimilation

通过数据同化进行小时内云指数预测

• DOI: 10.1016/j.solener.2019.03.065
• 发表时间: 2019
• 期刊: Solar Energy
• 影响因子: 6.7
• 作者: Harty, Travis M.;Holmgren, William F.;Lorenzo, Antonio T.;Morzfeld, Matthias
• 通讯作者: Morzfeld, Matthias

Approximation Algorithms for Priority Steiner Tree Problems

优先斯坦纳树问题的近似算法

• 发表时间: 2021
• 期刊: 27th International Computing and Combinatorics Conference (COCOON
• 作者: Spence, R.;Kobourov, S.;Sahneh, F.
• 通讯作者: Sahneh, F.