Active Sequential Change-Point Detection for High-dimensional Streaming Data

Thomas M. Menino Convention & Exhibition Center 

(Large-scale) streaming data are generated or encountered in many real-world applications ranging from biosurveillance and public/personal heath to environmental monitoring and network security to finance and economics and so on. Often one would like to utilize observed streaming data to make efficient sequential or quickest decision subject to the resources or sampling constraints in which the decision maker is responsible to actively choose partial data to be observed. In this talk, we present our latest research on active sequential change-point detection when monitoring high-dimensional streaming data, and apply multi-armed bandit (MAB) and federated learning to develop algorithms that are statistically efficient and computationally scalable under the sampling control. Asymptotic analysis, numerical studies, and their adaptions to case studies such as hot-spots detection of images or infectious diseases will be presented.

change-point

Active Learning

Streaming data

Sequential decision

multi-armed bandit

high-dimensional data