TRIPODS: Transdisciplinary Research Institute for Advancing Data Science (TRIAD)
TRIPODS: Transdisciplinary Research Institute for Advancing Data Science (TRIAD)
批准号:
1740776
负责人:
Xiaoming Huo
金额:
$150.0万
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
项目状态:
已结题
起止时间:
2017-09-01 至 2024-08-31
中文摘要
该项目在佐治亚理工学院建立了跨学科的高级数据科学研究所(TRAD)。Triad的目标是整合数据科学的数学、统计和算法基础方面的研究和教育。分析来自人类活动几乎每一个领域的海量、动态、嘈杂和复杂的数据是我们这个时代面临的紧迫挑战,也是一个对经济和社会影响非常重要的领域。Triad将解决在建立数据科学基础方面日益增长的挑战,数据科学的大部分基础位于计算机科学、统计学和数学的交叉点。Triad的学术重点是设计和建立跨学科研究计划,为思想和利益相关者(包括来自领域科学的理论家/科学家和技术用户)提供一个使能和交叉交流的平台。三合会主持重点工作小组、国内和国际研讨会,并组织创新实验室。参与者包括高级、职业生涯中期和初级教职员工、博士后研究员、研究生和高级本科生,以及广大数据科学从业者。所有三合会活动均涉及来自三个基础学科的跨学科人员。三合会运用资讯科技和通讯基建设施,迅速而有效地传播其研究和活动,而整个研究界亦可方便地查阅和评论三合会所选择的研究计划和课题。该研究所旨在创造一种知识氛围,定期联系来自全国和世界各地的理论家和实践者、科学家和工程师。TRIAD丰富了来自全国各地的参与者的职业生涯,从本科生到高级研究人员。博士后研究员和研究生被介绍到研究所活动中的合作研究中,并通过讲习班。三合会作出审慎的努力,接触不同的社区,包括来自规模较小的学院和服务于代表性不足的少数族裔的机构的参与者。三合会积极透过公开讲座、新闻稿及其他互联网渠道进行外展活动。Triad与相关的专业协会合作,为与数据科学相关的倡议提供刺激。其他活动(如定制工作坊)将结合互动项目和实地考察,让来自美国各地的本科生和/或高中生熟悉与数据科学相关的技术和三合会为期一年的项目的主题。将尽一切努力在网上提供产品和讲座,并使远程参与成为可能。该项目的资金来自CEISE计算与通信基金会和MPS数学科学部。
英文摘要
This project creates the Transdisciplinary Research Institute for Advancing Data Science (TRIAD) at the Georgia Institute of Technology. TRIAD aims to integrate research and education in mathematical, statistical, and algorithmic foundations for data science. Analysis of massive, dynamic, noisy, and complex data arising in virtually every sphere of human activity is a pressing challenge of our time, and an area of great importance for its economic and societal impact. TRIAD will address the growing challenges in establishing the foundations of data science, much of which lies at the intersection of computer science, statistics, and mathematics. TRIAD's intellectual focus is to design and build transdisciplinary research programs that provide an enabling and cross-fertilizing platform of ideas and stakeholders (including theoreticians/scientists from domain sciences and users of technology). TRIAD hosts focused working groups, national and international workshops, and organized innovation labs. Participants include senior, mid-career, and junior faculty members, postdoctoral fellows, graduate and senior undergraduate students, and data science practitioners at large. All TRIAD activities involve interdisciplinary personnel from the three foundational disciplines. TRIAD deploys information technology and communication infrastructure to quickly and efficiently disseminate its research and activities, while the research community at large can easily access and comment/critique TRIAD's choice of research programs and topics. The institute aims to create an intellectual atmosphere that connects theoreticians and practitioners, scientists, and engineers from across the nation and worldwide on a regular basis.TRIAD enriches careers of participants ranging from undergraduate students to senior researchers from around the nation. Postdoctoral fellows and graduate students are introduced to collaborative research in the institute activities and through workshops. TRIAD makes prudent efforts to reach out to diverse communities, including participants from smaller colleges and institutions serving under-represented minorities. TRIAD actively engages in outreach through public lectures, press releases, and dissemination via other internet channels. TRIAD works with associated professional societies to provide stimulus to data-science-related initiatives. Additional activities (such as customized workshops) will combine interactive projects and field trips to acquaint undergraduate and/or high school students from all over the U.S. with data-science-related techniques and the themes of TRIAD's year-long programs. Every effort will be made to make products and lectures available online and to enable remote participation. Funds for the project come from CISE Computing and Communications Foundations and MPS Division of Mathematical Sciences.
期刊论文(3)
专著(0)
科研奖励(0)
会议论文
Sequential Change Detection by Optimal Weighted ℓ₂ Divergence
通过最优加权 α 散度进行序列变化检测
DOI:
10.1109/jsait.2021.3072960
发表时间:
2021
期刊:
IEEE Journal on Selected Areas in Information Theory
影响因子:
--
作者:
[Xie, Liyan, Xie, Yao]
通讯作者:
Xie, Yao
DOI:
10.1007/s10589-021-00280-9
发表时间:
2019-05
期刊:
Computational Optimization and Applications
影响因子:
2.2
作者:
[Jiaming Liang;R. Monteiro;C. Sim]
通讯作者:
Jiaming Liang;R. Monteiro;C. Sim
DOI:
10.1109/jsait.2021.3072962
发表时间:
2021-04
期刊:
IEEE Journal on Selected Areas in Information Theory
影响因子:
--
作者:
[Liyan Xie;Shaofeng Zou;Yao Xie;V. Veeravalli]
通讯作者:
Liyan Xie;Shaofeng Zou;Yao Xie;V. Veeravalli
Theoretical Guarantees of Statistical Methodologies Involving Nonconvex Objectives and the Difference-Of-Convex-Functions Algorithms
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批准号:2015363
-
项目类别:Standard Grant
-
资助金额:$30.0万
-
财政年份:2020
-
负责人:Xiaoming Huo
-
依托单位:
CHE/DMS Innovation Lab: Learning the Power of Data in Chemistry
-
批准号:1848701
-
项目类别:Standard Grant
-
资助金额:$22.55万
-
财政年份:2018
-
负责人:Xiaoming Huo
-
依托单位:
Computational and Communication Efficient Distributed Statistical Methods with Theoretical Guarantees
-
批准号:1613152
-
项目类别:Continuing Grant
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资助金额:$37.5万
-
财政年份:2016
-
负责人:Xiaoming Huo
-
依托单位:
Workshop on the Algorithmic, Mathematical, and Statistical Foundations of Data Science
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批准号:1637436
-
项目类别:Standard Grant
-
资助金额:$10.0万
-
财政年份:2016
-
负责人:Xiaoming Huo
-
依托单位:
Fundamentals and Applications of Connect-the-Dots Methods
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批准号:0700152
-
项目类别:Standard Grant
-
资助金额:$24.87万
-
财政年份:2007
-
负责人:Xiaoming Huo
-
依托单位:
Statistical Problems in Detectability
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批准号:0604736
-
项目类别:Standard Grant
-
资助金额:$9.5万
-
财政年份:2006
-
负责人:Xiaoming Huo
-
依托单位:
ACT SGER: Locating Sparse Events in High Speed Stream Data, with a Focus on Statistical Analysis
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批准号:0346307
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项目类别:Standard Grant
-
资助金额:$10.0万
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财政年份:2003
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负责人:Xiaoming Huo
-
依托单位:
Collaborative Research: a Focused Research Group on Multiscale Geometric Analysis -- Theory, Tools, and Applications
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批准号:0140587
-
项目类别:Standard Grant
-
资助金额:$15.34万
-
财政年份:2002
-
负责人:Xiaoming Huo
-
依托单位:
Fifth North American Meeting of New Researchers in Statistics and Probability
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批准号:0096528
-
项目类别:Standard Grant
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资助金额:$1.5万
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财政年份:2001
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负责人:Xiaoming Huo
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依托单位:
海外基金