With the growing prevalence of sensors and other devices collecting data in real-time, automated data analysis methods with theoretically justified performance guarantees are in constant demand. Often a key question with such streaming data is whether they show evidence of anomalous behaviour. This could, e.g., be due to malicious bot activity on a website; early warning of potential equipment failure or detection of methane leakages. In such cases, These and other motivating examples share a common feature which is not accommodated by classical point anomaly models in statistics: the anomaly may not simply be an 'outlying' observation, but rather a distinctive pattern observed over consecutive observations. 
 
The strategic vision for this programme grant is to establish the statistical foundations for Detecting Anomalous Structure in Streaming data settings (DASS). The DASS programme brings together researchers from four of the UK's leading universities in statistics: Lancaster University, the London School of Economics and Political Science, the University of Bristol and the University of Warwick. This £4M initiative, funded by the Engineering and Physical Sciences Research Council (EPSRC), the four participating institutions, and several dedicated project partners, will run for five years (2024-2029).
 
A key element of DASS's approach is the active engagement of numerous partners, for whom the detection and interpretation of anomalous structures are vital. These partners represent sectors including Computing and Communication Technology, Energy and Environment, and Security.