Active Sequential Change-Point Analysis of Multi-Stream Data

该项目旨在开发高效的方法和算法，用于在采样或资源受限的情况下从高维流数据中进行主动学习。在许多现实世界的应用程序中，系统由许多进程组成，这些进程可以生成许多数据流。在某个未知的时间，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作的，这妨碍了所有资源的同时使用。因此，决策者必须负责主动选择优先观察哪些流程。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说明错误申报的惩罚。这项研究将在生物监测、流行病学、工程学、国土安全和金融等广泛的现实世界应用中产生更广泛的影响。该项目将通过将研究成果注入课程和培养研究生来整合研究和教育。该项目寻求在采样或资源限制下对多数据流数据进行主动序贯变点分析的方法学、理论和应用方面的全面进步。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)发展渐近理论来表征所开发方法的统计性能；(3)后继分析，应用错误发现率方法识别异常过程；(4)应用来自重症监护病房医疗传感器的在线监测数据进行脓毒症筛查，以在避免警报疲劳的同时尽可能快地识别脓毒症患者。该项目的成果预计将显著推进序列分析、变点检测、多臂匪徒问题、流数据分析和大规模推理方面的最新水平。该奖项反映了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 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.