Conference proposal: From Industrial Statistics to Data Science

“从工业统计到数据科学”会议将于2015年10月1日至3日在密歇根大学安娜堡举行。这次会议的主题是一条解决科学和工业领域下一代关键挑战的途径，统计学家可以在工业统计的强大遗产的基础上做出贡献，这是学术界和工业界许多研究人员做出贡献的结果。会议将有来自学术界和工业界的约20位受邀演讲者参加，他们中的许多人对工业统计、数据科学和统计方法的相关领域做出了根本贡献，并举行了学生海报会议。该奖项将为学生、博士后研究员和初级研究人员提供注册和参加会议的旅费。最近围绕术语“数据科学”的兴趣结晶提供了一个框架，用于思考如何有效地构建行业统计学家的贡献，并在现代应用程序中利用开发良好的经典框架。工业统计的重点是可靠性和实验设计，传统上一直是与工业有密切联系的统计领域。在数据科学时代，许多传统领域，如实验设计，一如既往地相关，但需要建立新的联系，将这些想法带入大数据的背景下。为了满足这一需求并帮助建立相关联系，会议计划将包括关于工业统计、数据科学以及解决自然科学、社会科学和工程学中具有挑战性的问题的新方法的会议，以期了解这些领域的研究如何为关键的国家和全球优先事项做出贡献。欲了解更多有关这次会议的信息，请访问https://sites.lsa.umich.edu/vn65.。

The conference "From Industrial Statistics to Data Science" will be held October 1-3, 2015, in Ann Arbor, Michigan, on the University of Michigan campus. The theme of the conference is a path toward addressing the next generation of critical challenges in science and industry to which statisticians can contribute, building upon the strong legacy of industrial statistics that has resulted from the contributions of many researchers in academia and in industry. The conference will feature approximately 20 invited speakers, from both academia and industry, many of whom have made fundamental contributions to industrial statistics, data science, and allied areas of statistical methodology, and a student poster session. This award will support registration and travel to the conference for students, postdoctoral fellows, and junior researchers. Recent crystallization of interest around the term "Data Science" provides a framework for thinking about ways to effectively build on the contributions of industrial statisticians and leverage the well developed classical frameworks in modern applications. Industrial statistics with its focus on reliability and design of experiments has traditionally been an area of statistics with strong connections to industry. In the era of data science, many of the traditional areas such as design of experiments are as relevant as ever, but new connections need to be made to bring these ideas into the context of big data. To address this need and help build relevant connections, the conference program will include sessions on industrial statistics, data science, and new methodologies for addressing challenging problems in natural science, social science, and engineering, with a view toward how research in these areas contributes to critical national and global priorities. More information about the conference can be found at https://sites.lsa.umich.edu/vn65.