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日在密歇根州安阿伯的密歇根大学校园举行。 会议的主题是，在学术界和工业界许多研究人员的贡献所产生的工业统计的强大遗产基础上，探索一条应对科学和工业领域下一代重大挑战的道路，统计人员可以为之做出贡献。 会议将邀请来自学术界和工业界的约20位演讲者，其中许多人对工业统计，数据科学和统计方法的相关领域做出了重要贡献，并将举办学生海报会议。 该奖项将支持学生，博士后研究员和初级研究人员的注册和旅行。 最近围绕“数据科学”一词的兴趣结晶提供了一个框架，用于思考如何有效地建立在工业统计学家的贡献基础上，并在现代应用中利用开发良好的经典框架。 工业统计，其重点是可靠性和设计的实验一直是一个领域的统计与工业有很强的联系。 在数据科学时代，许多传统领域（如实验设计）与以往一样相关，但需要建立新的联系，将这些想法带入大数据的背景中。 为了满足这一需求并帮助建立相关联系，会议计划将包括工业统计，数据科学和解决自然科学，社会科学和工程中具有挑战性问题的新方法的会议，以期这些领域的研究如何有助于关键的国家和全球优先事项。有关会议的更多信息，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.