ATD: Algorithms for Real-time Dynamic Risk Identification with Statistical Confidence

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

Recent advancements in digital technologies, such as wide-bandwidth networks, online marketplaces, large supply chain and logistics networks, widespread smartphone usage, wearable devices, and digital health technologies, have facilitated the generation and storage of near-real-time, high-resolution datasets. These datasets are sequentially available at a high frequency from a large number of subjects, spanning various fields including healthcare, medicine, mobile health, supply chain, and network monitoring. This type of data collection, commonly referred to as streaming data, has brought about a paradigm shift in technology and presents significant opportunities for real-time threat detection by monitoring data in motion and making continuous decisions in a timely manner. This research project leverages the potential of streaming data research by developing algorithms for real-time dynamic risk identification that fully explore the unique features of massive data streams. The project will provide research training opportunities for graduate students. The investigators aim to develop algorithms and statistical methods for real-time risk detection in streaming data, particularly in the domains of electronic medical records, mobile health, and supply chain. The developed approaches allow for dynamic revision of statistical models, efficient storage of summary statistics, and accurate detection of threats and abnormal behaviors as new data arrives. By incorporating dependent and non-identically distributed samples, the project moves away from simplistic models and embraces new frameworks to reflect domain problems and data realities. The approaches will be applied to address significant scientific questions in HIV prevention, mobile health with depression disorders, and supply chain disruptions. The project will create a unified framework for dynamic risk identification that can be readily incorporated into various disciplines, fostering collaborations with subject-matter scientists, and involving students in state-of-the-art research through educational initiatives. 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.