ATD: Multimode Machine Learning and Deep GeoNetworks for Anomaly Detection

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This research effort creates mathematical concepts and numerical methods for the analysis of spatiotemporal datasets with a special emphasis on anomaly detection. Early and accurate detection of unusual events and forecasts of future threats are critical in designing an effective response to them. Current algorithms for threat detection are often unable to keep up with the numerous demands, changing environments, and the huge amounts of spatiotemporal data that need to be processed and analyzed to accomplish these tasks. Uncertainty, scale, non-stationarity, noise, and heterogeneity are fundamental issues impeding progress at all phases of the pipeline that creates knowledge from data. The goal of this research effort is to develop novel mathematical concepts and computational methods that can detect anomalies in heterogenous, large-scale, spatiotemporal datasets. Beyond the project's broad technological impact, it serves as a model for the kind of cross-disciplinary activity critical for research and education at the mathematics/engineering frontier.The PI will devise efficient, robust, and scalable algorithms for unsupervised and semi-supervised learning. In particular, the PI will focus on the development of two approaches: (i) A multimodal diffusion framework for unsupervised prediction of anomalies from spatiotemporal data. Here, multimode refers to the fact that data may have different modalities, such as text, images, geolocations, etc. (ii) A scalable framework for semi-supervised learning on graph-structured data, based on the aforementioned multimodal diffusion framework and on a novel variant of deep convolutional networks specifically designed to operate on spatiotemporal data. The expected success of this project is based on existing achievements by the investigator in developing advanced mathematical concepts and turning them into real-world applications.