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TRIPODS: Institute for Foundations of Data Science (IFDS)

TRIPODS: Institute for Foundations of Data Science (IFDS)
TRIPODS:数据科学研究所 (IFDS)
批准号:
1740751
负责人:
Piotr Indyk
金额:
$136.85万
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:
2017-10-01 至 2021-09-30

项目摘要

项目成果

Piotr Indyk的其他基金

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中文摘要
翻译
这个项目的目的是为麻省理工学院一个致力于数据科学基础的跨学科研究所埋下种子。在第一阶段的三年中,该项目的目标是在麻省理工学院和整个研究界促进数学、统计学和理论计算机科学之间的研究和教育互动。在此过程中,团队还将开发(潜在的)第二阶段活动所需的组织能力和知名度。第一阶段的项目活动将围绕5个学期的主题进行组织。每个主题将致力于至少两个(通常是三个)三脚架区域相交的一个主题,并将重点放在催化它们之间的互动上。具体的主题是:统计和计算的权衡,亚线性算法和分布测试,复杂结构的学习,图形模型和互换性,以及非凸优化。在每个主题中,私人投资促进机构将举办许多活动,包括工作坊、介绍入门材料的秋季/春季学校和定期研讨会。他们还将支持知识传授活动,包括提供关于数据科学的算法、数学和统计方面的免费帮助和咨询的办公时间。为了促进参与者之间的互动,该项目将支持两名项目博士后,他们将扮演不同学科之间的连接者的角色。
英文摘要
The aim of this project is to plant the seeds for an interdisciplinary institute devoted to foundations of data science at MIT. During the three years of Phase I, the project goal is to stimulate research and educational interactions between mathematics, statistics and theoretical computer science, both within MIT and in the research community at large. On the way, the team will also develop the organizational capabilities and visibility needed for the (potential) Phase II activities.The project activities in Phase I will be organized around 5 semester-long themes. Each theme will be devoted to a topic at the intersection of at least two (and often three) TRIPODS areas, and will focus on catalyzing interactions between them. The specific themes are: statistical and computational tradeoffs, sub-linear algorithms and distribution testing, learning with complex structures, graphical models and exchangeability, and non-convex optimization. Within each theme, the PIs will organize numerous activities, including a workshop, a fall/spring school covering introductory material, and regular seminars. They will also support knowledge transfer activities, including office hours that will offer free help and consultation on algorithmic, mathematical and statistical aspects of data science. To facilitate the interactions between the participants, the project will support two project postdocs, who will play the role of connectors between different disciplines.
期刊论文(65)
专著(0)
科研奖励(0)
会议论文
Monotone probability distributions over the Boolean cube can be learned with sublinear samples
布尔立方体上的单调概率分布可以通过次线性样本来学习
DOI: --
发表时间: 2020
期刊: 11th Innovations in Theoretical Computer Science (ITCS 2020
影响因子: --
作者: [Rubinfeld, Ronitt, Vasilyan, Arsen]
通讯作者: Vasilyan, Arsen
DOI: 10.1016/j.cell.2019.01.006
发表时间: 2019-02-07
期刊: CELL
影响因子: 64.5
作者: [Schiebinger, Geoffrey, Shu, Jian, Lander, Eric S.]
通讯作者: Lander, Eric S.
Model Agnostic Time Series Analysis via Matrix Estimation
通过矩阵估计进行与模型无关的时间序列分析
DOI: 10.1145/3287319
发表时间: 2018
期刊: Proceedings of the ACM on Measurement and Analysis of Computing Systems
影响因子: --
作者: [Agarwal, Anish, Amjad, Muhammad Jehangir, Shah, Devavrat, Shen, Dennis]
通讯作者: Shen, Dennis
DOI: 10.1016/j.crma.2018.10.010
发表时间: 2018-09
期刊: Comptes Rendus Mathematique
影响因子: 0.8
作者: [P. Rigollet;J. Weed]
通讯作者: P. Rigollet;J. Weed
共 62 条
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    Conference: SODA 2023 Conference Student and Postdoc Travel Support
    Foundations of Data Science Institute
    Collaborative Research: AF: Small: Fine-Grained Complexity of Approximate Problems
    海外基金