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

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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.