Active Sequential Change-Point Analysis of Multi-Stream Data

该项目旨在开发有效的方法和算法，在采样或资源限制下从高维流数据中主动学习。在许多实际应用中，一个系统由许多进程组成，这些进程可以生成许多数据流。在某些未知的时间，系统可能会发生异常事件，例如疾病爆发、制造缺陷或欺诈信号，从而产生一组异常过程。然而，大多数系统都是在资源限制下运行的，这些资源限制阻止了所有时间同时使用所有资源。因此，决策者必须负责主动选择哪些过程优先进行观察。这将增强他们对正在发生的事件或异常过程的现有知识，同时探索新的信息并对错误声明的处罚进行解释。该研究将在生物监测、流行病学、工程、国土安全和金融等广泛的现实应用中产生更广泛的影响。本项目将通过将研究成果融入课程和培养研究生的方式，将研究与教育结合起来，力求在抽样或资源限制下的多流数据主动序贯变点分析的方法、理论和应用方面取得全面进展。 具体的研究目标是：（1）设计有效的具有虚警保证的主动变点检测算法，（2）建立一个渐近理论来描述所发展的方法的统计性能，（3）事后分析应用错误发现率方法来识别异常过程;以及（4）在脓毒症筛查中的应用，其具有来自重症监护病房中的医学传感器的在线监测数据，以尽可能快地识别脓毒症患者，同时避免警报疲劳。该项目的成果预计将大大推进序列分析，变点检测，多臂强盗问题，流数据分析和大规模推理的最新技术水平。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This project aims to develop efficient methodologies and algorithms for actively learning from high-dimensional streaming data under the sampling or resource constraints. In many real-world applications, a system consists of many processes that can generate many data streams. At some unknown time, an unusual event could occur to the system, for example, a disease outbreak, a manufacturing defect, or a fraud signal, yielding a set of anomalous processes. Most systems however are operated under resource constraints that prevent the simultaneous use of all resources all the time. Thus, the decision maker must be responsible for actively choosing which processes are prioritized for observation. This will enhance their existing knowledge about the occurring event or anomalous processes while exploring new information and accounting for the penalty of the wrong declaration. The research would have broader impacts in a wide range of real-world applications such as biosurveillance, epidemiology, engineering, homeland security, and finance. The project will integrate research and education by infusing research findings into the curriculum and by training graduate students.This project seeks to make comprehensive progress on methodology, theory, and application of active sequential change-point analysis of multi-stream data under the sampling or resource constraints. The specific research aims are: (1) design efficient active change-point detection algorithms with false alarm guarantees, (2) develop an asymptotic theory to characterize statistical performances of the developed methods, (3) post-hoc analysis to apply false discovery rate methods to identify anomalous processes; and (4) applications in sepsis screening with online monitoring data from medical sensors in intensive care units to identify sepsis patients as quickly as possible while avoiding alarm fatigue. Results of the project are expected to significantly advance the state of the art in sequential analysis, change-point detection, multi-armed bandit problems, streaming data analysis, and large-scale inference.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.