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HDR TRIPODS: UIC Foundations of Data Science Institute

HDR TRIPODS: UIC Foundations of Data Science Institute
HDR TRIPODS:UIC 数据科学研究所基础
批准号:
1934915
负责人:
Lev Reyzin
金额:
$150.0万
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
项目状态:
已结题
起止时间:
2019-10-01 至 2024-09-30

项目摘要

项目成果

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中文摘要
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英文摘要
The project creates a collaborative research institute combining aspects of mathematics, statistics, computer science, and engineering to study the foundations of data science at the University of Illinois at Chicago (UIC). The institute will be a collaboration between three departments: Computer Science (CS), Mathematics, Statistics, and Computer Science (MSCS), and Electrical and Computer Engineering (ECE). The institute will leverage the wide range of expertise among the investigators on this project in the three departments to bring the theoretical foundations of data science closer to the practice of data science. This involves studying idealized models of data, understanding inherent computational limits associated to these idealized models, and then developing models and methods that are robust to realistic models of uncertainty. The institute will also focus on training the next generation of researchers and will leverage the diversity of UIC, a large urban public research-intensive university with one of the most diverse student bodies in the country.The research aims to push the boundaries of the theory of data science by both gaining deeper understanding of idealized models and by building a theory around realistic models of data and computation. The themes pursued by this institute will include 1) the representation and structure of data; 2) machine learning and complexity; and 3) robustness and privacy. These themes will serve to link the theory and application of data science and to provide opportunities for the investigators to pool their expertise across the three disciplines of theoretical computer science, mathematical sciences, and electrical engineering. The specific activities of the research institute will include hosting themed research workshops, developing the UIC data science curriculum across the three departments, and fostering regional and industrial collaborations through partnerships with the Midwest Big Data Hub and the Discovery Partners Institute. Broader impacts of the institute will include applications of the proposed research to practical data science problems, the development of interdisciplinary data science courses spanning multiple departments, and increasing participation, especially of underrepresented groups, by broadly recruiting students from UIC's diverse community to study data science.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.
期刊论文(23)
专著(0)
科研奖励(0)
会议论文
Decomposing the Training of Deep Learned Turbo codes via a Feasible MAP Decoder
通过可行的 MAP 解码器分解深度学习 Turbo 码的训练
DOI: 10.1109/istc57237.2023.10273550
发表时间: 2023
期刊: IEEE
影响因子: --
作者: [Mulgund, A., Devroye, N., Turán, Gy., Žefran, M.]
通讯作者: Žefran, M.
DOI: 10.1007/s10472-020-09696-1
发表时间: 2020-03
期刊: Annals of Mathematics and Artificial Intelligence
影响因子: 1.2
作者: [D. Berend;A. Kontorovich;L. Reyzin;Thomas Robinson]
通讯作者: D. Berend;A. Kontorovich;L. Reyzin;Thomas Robinson
On the Geometry of Stable Steiner Tree Instances
关于稳定斯坦纳树实例的几何结构
DOI: --
发表时间: 2022
期刊: Canadian Conference on Computational Geometry
影响因子: --
作者: [Freitag, James, Mohammadi, Neshat, Potukuchi, Aditya, Reyzin, Lev]
通讯作者: Reyzin, Lev
Combining No-regret and Q-learning
结合无悔和 Q 学习
DOI: 10.5555/3398761.3398833
发表时间: 2020
期刊: AAMAS Conference proceedings
影响因子: --
作者: [Kash, Ian A., Sullins, Michael, Hofmann, Katja]
通讯作者: Hofmann, Katja
21
    Institute for Data, Econometrics, Algorithms and Learning (IDEAL)
    • 批准号:
      2217023
    • 项目类别:
      Continuing Grant
    • 资助金额:
      $318.0万
    • 财政年份:
      2022
    • 负责人:
      Lev Reyzin
    • 依托单位:
    EAGER: New Algorithms for Feature-Efficient Learning
    • 批准号:
      1848966
    • 项目类别:
      Standard Grant
    • 资助金额:
      $10.0万
    • 财政年份:
      2018
    • 负责人:
      Lev Reyzin
    • 依托单位:
    海外基金