Travel: Student Support for the 2023 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023)

旅行:2023 年 ACM SIGKDD 知识发现和数据挖掘会议 (KDD 2023) 的学生支持

基本信息

  • 批准号:
    2323492
  • 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:
    2023-08-01 至 2024-11-30
  • 项目状态:
    已结题

项目摘要

This project provides support for 25 U.S.-based graduate students to participate in the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), to be held in Long Beach, California, August 6-10, 2023. KDD is one of the premier conference in data mining, data science, and knowledge discovery in the world. It is a prestigious interdisciplinary conference that brings together researchers and practitioners every year, over 3,000 in recent years. KDD covers topics in the data mining lifecycle (including algorithms, software, systems, and applications), as well as artificial intelligence, machine learning, data management, and information retrieval. Students participating in the conference are exposed to the latest research developments and can attend hands-on workshops, tutorials, eye-opening keynotes, and presentations. A strong representation of U.S. students and researchers is essential in maintaining U.S. competitiveness in these important areas today and into the future. As in various STEM fields, increasing the diversity of participants in KDD research areas is an important goal. Since 2013, the KDD conference includes a Broadening Participation in Data Mining workshop. The selection committee will select recipients of the support based on merit and need while striving for diversity in the selection process. As an interdisciplinary conference, KDD attracts researchers and practitioners from academia, industry, and governments who work on all aspects of data mining and data science problems. It includes a highly competitive technical program, with regular peer-reviewed papers in the form of oral and poster presentations, as well as panel discussions and invited talks by leading experts in academia and industry. The conference places special emphasis on supporting students through training and mentoring by offering both undergraduate and doctoral student consortiums. These will provide a comprehensive multi-facet learning experience for students at all levels. This grant aims to help U.S.-based students overcome financial barriers that may prevent them from attending the conference. The award will be advertised on different sites and social media platforms, and the results will be announced on the KDD 2023 website (https://www.kdd.org/kdd2023/).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.
该项目支持25名美国研究生参加将于2023年8月6日至10日在加利福尼亚州长滩举行的第29届ACM SIGKDD知识发现和数据挖掘大会(KDD 2023)。KDD是世界上数据挖掘、数据科学和知识发现领域的主要会议之一。这是一个享有盛誉的跨学科会议,每年都有研究人员和实践者聚集在一起,近年来有3000多人参加。KDD涵盖数据挖掘生命周期中的主题(包括算法、软件、系统和应用程序),以及人工智能、机器学习、数据管理和信息检索。参加会议的学生可以接触到最新的研究进展,并可以参加实践研讨会、教程、令人大开眼界的主题演讲和演讲。美国学生和研究人员的强大代表对于保持美国现在和未来在这些重要领域的竞争力至关重要。正如在各个科学、技术和经济研究领域一样,增加知识发现研究领域参与者的多样性是一个重要目标。自2013年以来,知识发现大会包括一个更广泛参与的数据挖掘研讨会。遴选委员会将根据功绩和需要选择接受资助的人,同时努力在遴选过程中实现多样性。作为一个跨学科的会议,KDD吸引了来自学术界、工业界和政府的研究人员和实践者,他们致力于数据挖掘和数据科学问题的方方面面。它包括一个竞争激烈的技术项目,定期以口头和海报演示的形式发表同行评议的论文,以及由学术界和工业界的知名专家进行小组讨论和特邀演讲。会议特别强调通过提供本科生和博士生联盟的培训和指导来支持学生。这些将为各级学生提供全面、多方面的学习体验。这笔助学金旨在帮助美国学生克服可能阻止他们参加会议的经济障碍。该奖项将在不同的网站和社交媒体平台上进行广告,结果将在KDD 2023网站上公布(https://www.kdd.org/kdd2023/).This奖项反映了美国国家科学基金会的法定使命,并已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

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Agoritsa Polyzou其他文献

Learning Behavioral Pattern Analysis Based on Digital Textbook Reading Logs
基于数字教材阅读日志的学习行为模式分析
研究100連発(youtube動画13分35秒~22分09秒で研究紹介)
100个研究系列(YouTube视频中介绍的研究13分35秒至22分09秒)
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Chengjiu Yin;Zhuo Ren;Agoritsa Polyzou;and Yong Wang;Sakai T;山岸順一
  • 通讯作者:
    山岸順一
Grade prediction with models specific to students and courses

Agoritsa Polyzou的其他文献

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