ATD: Collaborative Research: Adaptive and Rapid Spatial-Temporal Threat Detection over Networks

该项目旨在开发创新的机器学习和统计算法，用于检测、预防和响应网络威胁。两个具体的应用是监测来自美国各地诊所网络的多重抗生素耐药（MDR）淋病的威胁，以及监测患者群中的HIV传播。该研究在许多其他实际应用中产生了影响，包括生物监测，工程，国土安全，金融和公共卫生，其中收集大规模时空数据流，旨在快速检测和预防威胁。该研究旨在开发关键的可扩展算法和方法，以有效地监控，分析和优化这些情况下的响应。此外，该项目还将通过将研究成果融入课程，并通过邀请博士生参与，将研究与教育结合起来。学生在研究。 该项目旨在开发创新的算法，通过结合时空模型，常微分方程（ODE）模型与变点检测，以及多臂强盗和集成方法，在网络上监视大规模时空数据时，进行快速威胁检测。具体而言，在三个相互关联的研究任务中开发了有效的可扩展算法，包括：（1）通过将“背景+异常+噪声”分解框架与顺序变点检测相结合来快速检测威胁;（2）通过应用多臂强盗算法和自适应采样来预测分析威胁，以在不断变化的环境中评估群体水平上增加的风险;以及（3）通过基于校准的ODE和数据驱动的时空模型开发嵌套集合模型，对威胁进行规范性分析，以便更好地评估预防/干预行动的效果。该项目的成果预计将大大推进时空模型、在线学习、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，被认为值得支持。

This project aims to develop innovative machine learning and statistical algorithms for detecting, preventing, and responding to threats over networks. Two concrete applications are monitoring the threat of multi-antibiotic-resistant (MDR) gonorrhea from a network of clinics across the United States and monitoring HIV transmission in clusters of patients. The research has impact in many other practical applications, including biosurveillance, engineering, homeland security, finance, and public health, where large-scale spatial-temporal data streams are collected with the aim of rapid detection and prevention of threats. The research aims to develop crucial scalable algorithms and methods to effectively and efficiently monitor, analyze, and optimize responses in these situations. In addition, the project will integrate research and education by infusing the research findings into the curriculum and by involving Ph.D. students in research. This project aims to develop innovative algorithms for rapid threat detection by combining spatial-temporal models, ordinary differential equation (ODE) models with change-point detection, and multi-armed bandit and ensemble methods when monitoring large-scale spatial-temporal data over networks. In particular, efficient scalable algorithms are developed in three interrelated research tasks, including (1) rapid detection of threats by combining a "background + anomaly + noise" decomposition framework with sequential change-point detection; (2) predictive analytics of threats by applying multi-armed bandit algorithms and adaptive sampling in the changing environments to assess increasing risks at the population level; and (3) prescriptive analytics of threats by developing nested ensemble models based on calibrated ODE and data-driven spatial-temporal models so as to better assess the effects of prevention/intervention actions. Results of the project are expected to significantly advance the state of the art in spatial-temporal models, online learning, 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.