CAREER: Methods for Data-Driven Service Engineering

该学院早期职业发展计划(CALEAR)项目通过研究服务系统的数据驱动工程的基本原理，为促进国家的健康、繁荣和福利做出贡献。根据美国联邦储备委员会和劳工统计局的数据，2020年，医疗保健、通信、运输和物流等服务业占美国国内生产总值的67%以上，就业人口超过78%。设计服务系统以提高运营效率和成本效益是一项对美国具有重大社会经济影响的关键任务。设计和运营这些系统受益于这些系统复杂的数学模型的使用。然而，找出一个适合利益体系的模型是一个非常困难的问题。该奖项支持对如何利用大型操作数据集和新兴的机器学习技术来促进此类模型识别的基本理解。该奖项的研究产品将使工程师、政策制定者和院士能够更好地提高运营和成本效率，从而提高整体国民福利。作为该奖项的一部分，该教育计划旨在通过利用机器学习来预测学生表现并使学生导师和教师导师能够实施早期建议干预，从根本上改善大型公立大学和学院的学生咨询和辅导。这些干预措施可以显著提高学生留校率和毕业率，特别是在第一代大学生等高危人群中。大型服务系统表现出多尺度的时空变化，并且它们自然地使用在随机和非平稳环境中运行的随机网络模型来建模。模型识别和校准是数据驱动工程的基础。这一研究项目填补了文献中的一个重要空白，即大多数人关注的是静态模型。该奖项的研究提出了在随机和非平稳环境中运行的随机网络模型中用于模型校准的新的半参数方法的开发。这项研究利用并扩展了来自随机分析、度量空间上的优化和点过程理论的最新技术来开发这些方法。在模型推理的情况下，该项目研究了建立在M-估计、极小极大和函数逼近分析的基础上的理论，并将其显著扩展到服务系统模型的半参数设置。随机网络模型的复杂性为理解哪类这样的模型可以被唯一地识别创造了条件。该项目致力于可辨识性分析，并利用信息几何学来理解模型空间的景观或流形，以解决这一问题。总而言之，这些理论和方法将导致一种用于校准服务系统模型的全面的“灰盒”方法。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development Program (CAREER) project contributes to the advancement of the Nation's health, prosperity, and welfare by studying the fundamentals of data-driven engineering of service systems. Service industries such as healthcare, communications, transportation and logistics, comprised over 67 percent of US gross domestic product and employed more than 78 percent of the working population in 2020, according to the US Federal Reserve and the Bureau of Labor Statistics. Engineering service systems to increase operational efficiency and cost effectiveness is a crucial task with significant socio-economic implications for the US Designing and operating these systems has benefited from the use of sophisticated mathematical models of these systems. However, identifying a model appropriate to a system of interest is a remarkably hard problem. This award supports a fundamental understanding of how to utilize large operational data sets and emerging machine learning technologies, to facilitate such model identification. The research products of this award will enable engineers, policy makers and academicians to better improve operational and cost efficiencies, and as a consequence improve overall national welfare. The educational plan as part of this award aims to radically improve student advising and mentoring in large, public universities and colleges, by utilizing machine learning to predict student performance and enable student advisors and faculty mentors to implement early advising interventions. These interventions can substantially improve student retention and graduation rates, particularly among at-risk populations such as first-generation college students.Large service systems exhibit multi-scale spatiotemporal variations and they are naturally modeled using stochastic network models operating in random and nonstationary environments. Model identification and calibration are fundamental to data-driven engineering. This research project fills an important gap in the literature, where most of the focus is on stationary models. The research in this award proposes the development of novel semiparametric methods for model calibration in stochastic network models operating in random and nonstationary environments. The research leverages and extends state-of-art techniques from stochastic analysis, optimization over measure spaces and point process theory to develop these methods. In the case of model inference, the project investigates theory that builds on and significantly extends M-estimation, minimax and function approximation analyses to the semiparametric setting of service system models. The complex nature of stochastic network models creates a desideratum for an understanding of which classes of such models can be uniquely identified. The project addresses identifiability analyses and utilizes information geometry to understand the landscape or manifolds of the model space to address this question. Together, the theory and methods will lead to a comprehensive ‘graybox’ methodology for calibrating service system models.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.