Conference: Statistical Foundations of Data Science and their Applications

会议:数据科学的统计基础及其应用

基本信息

  • 批准号:
    2304646
  • 负责人:
  • 金额:
    $ 2.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-02-01 至 2024-01-31
  • 项目状态:
    已结题

项目摘要

This award supports a diverse and inclusive three-day conference titled "Statistical Foundations of Data Science and their Applications" to take place at Princeton University on May 8-10, 2023. Data science is a thriving broad discipline that combines various existing fields including classical and modern statistics, biostatistics, econometrics and machine learning, and has by now transformed the way that quantitative research is conducted in the social, behavioral and biomedical sciences, as well as in finance, industry and government more generally. The main goal of the conference is to bring together junior and senior scholars working on all aspects of foundational and applied data science, while also offering unique opportunities for mentoring junior and underrepresented scholars (e.g., underrepresented minorities, women, and persons with disabilities) across a broad range of disciplines. While data science combines and potentiates the best of many scientific areas of study, it is regrettably not always the case that scholars working of those specific areas interact with each other in a synergistic way. Furthermore, for young scholars it is often hard to reach out outside their subfields, hampering their intellectual and professional development. These intellectual barriers sometimes reduce diversity and inclusion due to the socially inefficient intellectual silos present in different academic and professional communities. A key goal of the conference is to be highly interdisciplinary and open to new intellectual ideas and approaches, hoping to reach out to academia, industry and government. Another equally important and highly complementary key goal of the conference is to foster junior and underrepresented scholars by offering them specifically tailored activities to such goal, in addition to offering them opportunities to interact and network with many top data science scholars from around the world that will be in attendance. The website with details about the conference is https://orfe.princeton.edu/events/dsconf/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.
该奖项支持将于2023年5月8日至10日在普林斯顿大学举行的为期三天的会议,题为“数据科学及其应用的统计基础”。数据科学是一门蓬勃发展的广泛学科,它结合了包括古典和现代统计学、生物统计学、计量经济学和机器学习在内的各种现有领域,到目前为止已经改变了社会、行为和生物医学科学以及更广泛的金融、工业和政府领域进行定量研究的方式。会议的主要目标是将从事基础和应用数据科学各方面工作的初级和高级学者聚集在一起,同时也为指导初级和代表性不足的学者(例如,代表性不足的少数族裔、妇女和残疾人)提供了广泛的学科领域的独特机会。虽然数据科学结合并加强了许多科学研究领域中最好的东西,但遗憾的是,从事这些特定领域工作的学者之间并不总是以协同的方式相互作用。此外,对于年轻学者来说,往往很难走出自己的子领域,这阻碍了他们的智力和专业发展。这些知识障碍有时会减少多样性和包容性,原因是不同的学术和专业团体中存在着社会效率低下的知识孤岛。会议的一个关键目标是高度跨学科,对新的知识想法和方法持开放态度,希望接触到学术界、工业界和政府。会议的另一个同样重要且互补性很强的关键目标是通过为他们提供专门为这一目标量身定做的活动来培养初级和代表性不足的学者,此外还为他们提供与将出席的许多来自世界各地的顶尖数据科学学者互动和建立网络的机会。关于这次会议的详细信息的网站是https://orfe.princeton.edu/events/dsconf/This奖,该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的智力优势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

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Matias Cattaneo其他文献

A Permutation Test and Estimation Alternatives for the Regression Kink Design
回归扭结设计的排列测试和估计替代方案
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alberto Abadie;David Card;Matias Cattaneo;Raj Chetty;Avi Feller;Edward Glaeser;Paul Goldsmith;Guido Imbens;Maximilian Kasy;Larry Katz;Zhuan Pei;Mikkel Plagborg;Guillaume Pouliot
  • 通讯作者:
    Guillaume Pouliot

Matias Cattaneo的其他文献

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{{ truncateString('Matias Cattaneo', 18)}}的其他基金

Partitioning-Based Learning Methods for Treatment Effect Estimation and Inference
基于分区的治疗效果估计和推理学习方法
  • 批准号:
    2241575
  • 财政年份:
    2023
  • 资助金额:
    $ 2.5万
  • 项目类别:
    Standard Grant
Nonparametric Estimation and Inference with Network Data
网络数据的非参数估计和推理
  • 批准号:
    2210561
  • 财政年份:
    2022
  • 资助金额:
    $ 2.5万
  • 项目类别:
    Standard Grant
New Developments in Methodology for Program Evaluation
项目评估方法的新进展
  • 批准号:
    2019432
  • 财政年份:
    2020
  • 资助金额:
    $ 2.5万
  • 项目类别:
    Standard Grant
Collaborative Research: Robust Inference for Kernel Smoothing and Related Problems
协作研究:核平滑及相关问题的鲁棒推理
  • 批准号:
    1947805
  • 财政年份:
    2020
  • 资助金额:
    $ 2.5万
  • 项目类别:
    Standard Grant
A Random Attention Model: Identification, Estimation and Testing
随机注意力模型:识别、估计和测试
  • 批准号:
    1628883
  • 财政年份:
    2016
  • 资助金额:
    $ 2.5万
  • 项目类别:
    Standard Grant
Collaborative Research: Flexible and Robust Data-driven Inference in Nonparametric and Semiparametric Econometrics
协作研究:非参数和半参数计量经济学中灵活且稳健的数据驱动推理
  • 批准号:
    1459931
  • 财政年份:
    2015
  • 资助金额:
    $ 2.5万
  • 项目类别:
    Standard Grant
New Methodological Developments for Inference in the Regression-Discontinuity Design
回归-不连续性设计中推理的新方法论发展
  • 批准号:
    1357561
  • 财政年份:
    2014
  • 资助金额:
    $ 2.5万
  • 项目类别:
    Standard Grant
Collaborative Research: Non-Standard Asymptotic Theory for Semiparametric Estimators
合作研究:半参数估计的非标准渐近理论
  • 批准号:
    1122994
  • 财政年份:
    2011
  • 资助金额:
    $ 2.5万
  • 项目类别:
    Standard Grant
Collaborative Research: Small Bandwidth Asymptotic Theory for Kernel-Based Semiparametric Estimators
合作研究:基于核的半参数估计器的小带宽渐近理论
  • 批准号:
    0921505
  • 财政年份:
    2009
  • 资助金额:
    $ 2.5万
  • 项目类别:
    Standard Grant

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