Conference: Quality and Productivity Research Conference - Statistics, Deep Learning, and the People Side of Process

会议：质量和生产力研究会议 - 统计、深度学习和流程的人员方面

• 批准号: 2312733
• 负责人: Jamison Kovach
• 金额: $ 2.2万
• 依托单位: University of Houston
• 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: 2023-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312733&HistoricalAwards=false
• 关键词: Conference; Quality; Productivity; Research; Statistics

The 39th Quality and Productivity Research Conference (QPRC) will be hosted by the University of Houston (UH) in Houston, Texas from June 6-8, 2023. It is the main annual event for the Quality and Productivity Section of the American Statistical Association, and it will consider data science and statistics related topics though presentations, poster sessions, a technical tour, and a one-day short course. Because billions of data sets are collected, processed, and analyzed on a daily basis in virtually every environment known, with an increasing number of data-based decisions being made that have real-world consequences for individuals and society, the data science and statistics community must keep pace with the rapid growth and variety of collected data and provide up-to-date methodologies and guidance to every applied field that utilizes data. QPRC aims to support knowledge sharing regarding data science and statistics to enable researchers and practitioners to understand the impact of data-based systems and decisions, and avoid, or at least detect and mitigate, unintended adverse consequences. It will provide a unique opportunity for attendees to meet, exchange ideas and experiences, and form collaborations. Through participation in QPRC students will gain access to invaluable learning experiences and networking opportunities with other conference attendees. The conference theme is “Statistics, Deep Learning & the People Side of Process,” and it will include 18 invited paper sessions, four to six contributed sessions, poster sessions, a technical tour, and a one-day short course (on June 5). QPRC has the potential to advance knowledge and understanding of topics related to data science and statistics by providing a unique opportunity for statisticians, data scientists, quantitative analysts, researchers, and practitioners to discuss the current progress made in computer-intensive fields such as machine learning, facial recognition, and so forth, and exchange novel ideas and experiences in working with modern big data. Hence, this conference has the potential to 1) disseminate new methods and data-driven approaches, the evaluation of previous findings, and the validation of theoretical approaches, 2) stimulate further investigations regarding the benefits of working with big, multidimensional data, both structured and unstructured, and 3) increase the awareness of the need to use big data ethically and to address the bias that may result from the automated collection and analysis of large datasets. In addition, QPRC has the potential to benefit society in several ways. First, it provides the opportunity for attendees to learn and reframe their understanding of concepts related to data science and statistics. Second, QPRC will promote the responsible deployment and interpretation of data science and statistical methods in a variety of applied areas. Third, to disseminate the knowledge from the conference to the broader community, QPRC presenters will be invited to submit their work for publication in a special issue of the Journal of Applied Stochastic Models in Business and Industry. Fourth, to broaden the participation of underrepresented groups (i.e., women, racial/ethnic minorities, etc.) in science, technology, engineering, and mathematics (STEM) disciplines, funding is requested to support graduate students especially those in underrepresented groups from U.S. institutions to participate in QPRC. The conference website is www.uh.edu/qprc2023.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.

第39届质量和生产力研究会议（QPRC）将于2023年6月6日至8日在德克萨斯州休斯顿由休斯顿大学（UH）主办。它是美国统计协会质量和生产力部门的主要年度活动，它将通过演讲，海报会议，技术参观和为期一天的短期课程来考虑数据科学和统计相关主题。由于在几乎所有已知的环境中，每天都要收集、处理和分析数十亿个数据集，越来越多的基于数据的决策对个人和社会产生了现实影响，因此数据科学和统计界必须跟上所收集数据的快速增长和多样性，并为利用数据的每个应用领域提供最新的方法和指导。QPRC旨在支持有关数据科学和统计的知识共享，使研究人员和从业人员能够了解基于数据的系统和决策的影响，并避免或至少检测和减轻意外的不良后果。它将为与会者提供一个独特的机会，以满足，交流思想和经验，并形成合作。通过参加QPRC，学生将获得宝贵的学习经验和与其他与会者建立联系的机会。会议主题是“统计，深度学习过程中的人员方面”，将包括18个邀请论文会议，4到6个贡献会议，海报会议，技术参观和为期一天的短期课程（6月5日）。QPRC有可能通过为统计学家，数据科学家，定量分析师，研究人员和从业人员提供一个独特的机会来促进对数据科学和统计学相关主题的知识和理解，讨论当前在机器学习，面部识别等计算机密集型领域取得的进展，并交流与现代大数据合作的新想法和经验。因此，本次会议有可能：1）传播新方法和数据驱动的方法，评估以前的研究结果，并验证理论方法，2）促进进一步研究使用结构化和非结构化的大多维数据的好处，3）提高对道德使用大数据的必要性的认识，并解决自动收集和分析大型数据集可能导致的偏见。此外，QPRC有可能以多种方式造福社会。首先，它为与会者提供了学习和重新构建他们对数据科学和统计学相关概念的理解的机会。第二，QPRC将促进数据科学和统计方法在各种应用领域的负责任部署和解释。第三，为了将会议的知识传播给更广泛的社区，QPRC的演讲者将被邀请提交他们的工作，以便在《商业和工业应用随机模型杂志》的特刊上发表。第四，扩大代表性不足群体的参与（即，妇女、少数种族/族裔等）在科学，技术，工程和数学（STEM）学科，资金被要求支持研究生，特别是那些来自美国机构的代表性不足的群体参加QPRC。会议网站是www.uh.edu/qprc2023.This奖反映了NSF的法定使命，并已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的支持。