Conference: The 2024 Joint Research Conference on Statistics in Quality, Industry, and Technology (JRC 2024) - Data Science and Statistics for Industrial Innovation
会议:2024年质量、工业和技术统计联合研究会议(JRC 2024)——数据科学与统计促进产业创新
基本信息
- 批准号:2404998
- 负责人:
- 金额:$ 1.2万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2024
- 资助国家:美国
- 起止时间:2024-03-15 至 2025-02-28
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
This project funds U.S. based student participation in the 2024 Joint Research Conference on Statistics in Quality, Industry and Technology (JRC 2024), which will be held in Waterloo, Ontario, Canada, from June 17-20, 2024, at the University of Waterloo. The organization of this conference is also in partnership with Virginia Tech. JRC 2024 is a joint meeting of the 29th Spring Research Conference on Statistics in Industry and Technology (SRC) and the 40th Quality and Productivity Research Conference (QPRC), which happens once every four years. The theme of JRC 2024 is "Data Science and Statistics for Industrial Innovation." JRC 2024 aims to bring together researchers and practitioners worldwide who use statistics in quality, technology, and industrial contexts. The conference promotes communication among researchers and practitioners to enable and ensure the development and widespread use of novel insights and methodology. JRC 2024 has the potential to benefit society by offering participants the opportunities to gain knowledge and reshape their perspectives on topics associated with data science, statistics, and machine learning. JRC 2024 will advocate for the ethical application and understanding of data science, statistics, and machine learning for industrial innovation, which is beneficial for the long-term competitiveness of the U.S. industry. The conference provides a platform to disseminate knowledge to the broader community by sharing short course lecture notes, presentation slides, and posters on the conference website. JRC 2024 aims to broaden the participation of underrepresented groups (i.e., women, racial/ethnic minorities, etc.) in STEM disciplines. JRC 2024 focuses on recent advancements in methodology, best practices, and innovative applications. Participation in JRC 2024 has the potential to advance knowledge and understanding of topics related to data science, statistics, and machine learning and how they can be relevant to industrial innovation. This conference traditionally attracts prominent statisticians, data scientists, quantitative analysts, and others with an established record of highly influential, methodological, and interdisciplinary research. These individuals will have the opportunity to discuss the current progress made in statistics and machine learning, such as big data technology, text modeling, the use of generative artificial intelligence in industrial innovation, and exchange novel ideas and experiences in working with modern data science to discover knowledge and apply it to numerous fields. JRC 2024 has the potential to disseminate new methods and data-driven approaches, the evaluation of previous findings, and the validation of theoretical approaches, stimulate further investigations regarding the benefits of working with statistics and machine learning methods for industry and increase the awareness of the need to use data science approach in industry. The conference will include three plenary presentations, 18 invited paper sessions, four to six contributed sessions, a poster session, a technical tour, and a one-day short course. More details on the conference can be found on its web page: https://uwaterloo.ca/joint-research-conference-statistics-quality-industry-technology/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.
该项目资助美国学生参加2024年质量,工业和技术统计联合研究会议(JRC 2024),该会议将于2024年6月17日至20日在加拿大安大略滑铁卢大学举行。这次会议的组织也与弗吉尼亚理工大学合作。JRC 2024是第29届工业和技术统计春季研究会议(SRC)和第40届质量和生产力研究会议(QPRC)的联席会议,每四年举行一次。JRC 2024的主题是“数据科学和统计促进工业创新”。“JRC 2024旨在汇集全球在质量,技术和工业背景下使用统计数据的研究人员和从业人员。会议促进研究人员和从业人员之间的沟通,以确保新的见解和方法的发展和广泛使用。JRC 2024有可能通过为参与者提供机会来获得知识并重塑他们对数据科学,统计学和机器学习相关主题的观点来造福社会。JRC 2024将倡导数据科学、统计学和机器学习在工业创新中的道德应用和理解,这有利于美国工业的长期竞争力。会议提供了一个平台,通过在会议网站上分享短期课程讲义,演示幻灯片和海报,向更广泛的社区传播知识。JRC 2024旨在扩大代表性不足群体的参与(即,妇女、少数种族/族裔等)在STEM学科。JRC 2024专注于方法,最佳实践和创新应用的最新进展。参加JRC 2024有可能促进对数据科学,统计学和机器学习相关主题的知识和理解,以及它们如何与工业创新相关。该会议传统上吸引了杰出的统计学家,数据科学家,定量分析师和其他具有高度影响力,方法论和跨学科研究的人。这些人将有机会讨论当前在统计和机器学习方面取得的进展,例如大数据技术,文本建模,在工业创新中使用生成式人工智能,并交流与现代数据科学合作的新想法和经验,以发现知识并将其应用于众多领域。JRC 2024有可能传播新方法和数据驱动的方法,评估以前的研究结果,验证理论方法,促进进一步调查统计和机器学习方法对工业的好处,并提高对在工业中使用数据科学方法的必要性的认识。会议将包括三个全体会议,18个邀请论文会议,四到六个贡献会议,海报会议,技术参观和为期一天的短期课程。关于会议的更多细节可以在其网页上找到:https://uwaterloo.ca/joint-research-conference-statistics-quality-industry-technology/This奖项反映了NSF的法定使命,并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。
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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Yili Hong其他文献
Product Component Genealogy Modeling and Field‐failure Prediction
产品组件谱系建模和现场故障预测
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:2.3
- 作者:
Caleb King;Yili Hong;W. Meeker - 通讯作者:
W. Meeker
On Computing the Distribution Function for the Sum of Independent and Non-identical Random Indicators Yili Hong
- DOI:
- 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Yili Hong - 通讯作者:
Yili Hong
Motivating User-Generated Content with Performance Feedback: Evidence from Randomized Field Experiments
通过性能反馈激励用户生成的内容:来自随机现场实验的证据
- DOI:
10.2139/ssrn.2971783 - 发表时间:
2017-05 - 期刊:
- 影响因子:5.4
- 作者:
Ni Huang;Gordon Burtch;Bin Gu;Yili Hong;Chen Liang;Kanliang Wang;Dongpu Fu;Bo Yang - 通讯作者:
Bo Yang
User idea implementation in open innovation communities: Evidence from a new product development crowdsourcing community
开放创新社区中的用户创意实施:来自新产品开发众包社区的证据
- DOI:
10.1111/isj.12286 - 发表时间:
2020-03 - 期刊:
- 影响因子:6.4
- 作者:
Qian Liu;Qianzhou Du;Yili Hong;Weiguo Fan;Shuang Wu - 通讯作者:
Shuang Wu
Effective Nonparametric Distribution Modeling for Distribution Approximation Applications
分布近似应用的有效非参数分布建模
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
T. Lux;L. Watson;Tyler H. Chang;Li Xu;Yueyao Wang;Jon Bernard;Yili Hong;K. Cameron - 通讯作者:
K. Cameron
Yili Hong的其他文献
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{{ truncateString('Yili Hong', 18)}}的其他基金
Doctoral Dissertation Research in DRMS: Expectation Bias and the Gender Wage Gap in the Online Gig Economy
DRMS 博士论文研究:在线零工经济中的期望偏差和性别工资差距
- 批准号:
1824432 - 财政年份:2018
- 资助金额:
$ 1.2万 - 项目类别:
Standard Grant
Reliability Prediction Based on Dynamic Data Collected with Modern Technology
基于现代技术采集的动态数据的可靠性预测
- 批准号:
1068933 - 财政年份:2011
- 资助金额:
$ 1.2万 - 项目类别:
Standard Grant
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