CAREER: Methods for Data-Driven Service Engineering

该教师早期职业发展计划（CAREER）项目通过研究服务系统的数据驱动工程的基本原理，为国家的健康，繁荣和福利的进步做出贡献。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，雇用了超过78%的劳动人口。工程服务系统，以提高运营效率和成本效益是一个重要的任务，具有显着的社会经济影响，美国设计和运营这些系统受益于使用这些系统的复杂的数学模型。然而，确定一个模型适合于感兴趣的系统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机器学习技术的基本理解，以促进此类模型识别。该奖项的研究成果将使工程师，政策制定者和学者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，教育计划旨在通过利用机器学习来预测学生的表现，并使学生顾问和教师导师能够实施早期咨询干预，从而从根本上改善大型公立大学和学院的学生咨询和指导。这些干预措施可以大大提高学生的保留率和毕业率，特别是在高危人群，如第一代大学生。大型服务系统表现出多尺度时空变化，他们自然是随机网络模型在随机和非平稳环境中运行。 模型识别和校准是数据驱动工程的基础。该研究项目填补了文献中的一个重要空白，其中大部分重点是静态模型。 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模型校准的新半参数方法的发展。该研究利用并扩展了随机分析，测量空间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，该项目研究的理论，建立在M-估计，极大极小和函数逼近分析的半参数设置的服务系统模型，并显着扩展。随机网络模型的复杂性创造了一个迫切需要的理解，这类模型可以唯一识别。该项目解决了可识别性分析，并利用信息几何来理解模型空间的景观或流形，以解决这个问题。 该奖项反映了NSF的法定使命，并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来支持。

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.