【Abstract】In advanced manufacturing systems, the rapid advances in cyber-infrastructure ranging from sensor technology and communication networks to high-powered computing have resulted in temporally and spatially dense data-rich environments. With massive data readily available, there is a pressing need to develop advanced methodologies and associated tools that will enable and assist (i) the handling of the rich data streams communicated by the contemporary complex engineering systems, (ii) the extraction of pertinent knowledge about the environmental and operational dynamics driving these systems, and (iii) the exploitation of the acquired knowledge for more enhanced design, analysis, diagnosis, and control of them.

Addressing this need is considered very challenging because of a collection of factors, which include the inherent complexity of the physical system itself and its associated hardware, the uncertainty associated with the system’s operation and its environment, the heterogeneity and the high dimensionality of the data communicated by the system, and the increasing expectations and requirements posed by real-time decision-making. It is also recognized that these significant research challenges, combined with the extensive breadth of the target application domains, will require multidisciplinary research and educational efforts.

This presentation will propose the engineering-driven data fusion concepts, and discuss some research challenges, advancements, and opportunities in synergies of engineering and statistics for system performance improvement. Specific examples will be provided on research activities related to the integration of statistics and engineering knowledge in various applications. Real case studies will be provided to illustrate the key steps of system research and problem solving, including (1) the identification of the real need and potential in problem formulation; (2) acquisition of a system perspective of the research; (3) development of new methodologies through interdisciplinary methods; and (4) implementation in practice for significant economical and social impacts. The presentation will emphasize the examples of research achievements, as well as how the achievements were achieved.

【个人简介】史建军博士是佐治亚理工学院工业与系统工程学院和机械工程学院联合聘任的Carolyn J. Stewart 讲席教授。他曾是密歇根大学工程学院G. Lawton and Louise G. Johnson Professor讲席教授。史教授分别在1980年毕业于河北石家庄第一中学，1984年和1987年于北京理工大学获自动化工程本科、硕士学位，1992年于密歇根大学获机械工程博士学位。

无论在美国还是国际上，史建军教授都是工业大数据、智能制造、质量科学和工业工程领域享有盛名的学者和学科带头人。他的研究成果具有十分重要的理论和现实意义，并且产生了深远的影响。其显著特点是具有突出的学术开拓性、学科奠基性、技术创新性和产业应用性。他累计发表论文180多篇，获得科研总经费2000多万美元。他也是为数不多的在基础研究、技术实现和工业应用方面都做出杰出贡献的学者。在研究领域，史教授强调融合工程知识、高等统计、系统信息化和控制理论等开发创新性的理论和方法，为制造和服务产品设计、质量控制和系统改善奠定基础。史教授建立的“变异流理论”、多层次结构模型和数据融合分析方法等，为多工序制造过程的误差分析和质量控制提供了一套全新的理论体系，改变了过去只能针对各个工序孤立进行误差分析和事后被动的缺陷检测的传统做法，被美国工业企业广泛应用。

史教授还是美国质量统计与可靠性学会发起人，是美国工业与系统工程学会旗舰刊物IISE Transactions的主编，和多个顶级期刊的副主编。在教学领域，史教授把他的研究成果和应用案例及时编写成教学题材，开设了一些独创性的新课程。得益于史教授高质量的指导，在他培养的博士生中有7人获得NSF CAREER奖，有一人获得“美国总统奖”。史教授出色的教育和科研成果为他赢得了NSF CAREER奖、IISE David F. Baker 杰出科研奖, IIE Albert G. Holzman 杰出教育奖等重大奖项，并当选为美国工业与系统工程学会（IISE）会士、美国机械工程学会（ASME）会士，美国运筹与管理科学学会（INFORMS）的会士，国际质量科学院（IAQ）院士, 和美国工程院（NAE）院士。