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多元化、包容性的会议,题为“数据科学的统计基础及其应用”。数据科学是一门蓬勃发展的广泛学科,它结合了包括经典和现代统计学、生物统计学、计量经济学和机器学习在内的各种现有领域,迄今为止已经改变了社会科学、行为科学和生物医学科学以及生物医学领域进行定量研究的方式。 更广泛地说,是在金融、工业和政府领域。会议的主要目标是聚集从事基础和应用数据科学各个方面工作的初级和高级学者,同时还为跨广泛学科的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)提供独特的机会。虽然数据科学结合并增强了许多科学研究领域的优点,但遗憾的是,这些特定领域的学者并不总是以协同的方式相互作用。此外,对于年轻学者来说,往往很难接触到自己的子领域之外,这阻碍了他们的智力和专业发展。由于不同学术和专业社区中存在社会效率低下的知识孤岛,这些知识障碍有时会降低多样性和包容性。会议的一个关键目标是高度跨学科,对新的思想和方法持开放态度,希望能够接触到学术界、工业界和政府。会议的另一个同样重要且高度互补的关键目标是通过为初级学者和代表性不足的学者提供专门定制的活动来培养他们,此外还为他们提供与将出席的世界各地的许多顶级数据科学学者互动和建立联系的机会。有关会议详细信息的网站是 https://orfe.princeton.edu/events/dsconf/ 该奖项反映了 NSF 的法定使命,并通过使用基金会的智力价值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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