Martin is a graduate student in Data Science and Artificial Intelligence at the University of Exeter, UK. His research focuses on time-series modelling and machine learning-based predictive modelling for water distribution management. A key area of interest is the forecasting of water leakages and water flow demands, helping decision-makers to take preventative action. This work aligns with supporting water companies in meeting Ofwat and the UK government's future leakage reduction targets as well as leveraging new technologies to contribute to healthier management of water distribution.

Jay is a graduate student in Data Science and Artificial Intelligence at the University of Exeter, UK. His research centers on the structural health monitoring of offshore wind turbine blades through deep learning modelling. By leveraging convolutional, recurrent, and transformer neural networks, Jay aims to develop advanced machine-learning models that enhance the reliability and efficiency of wind energy systems. His interdisciplinary work involves time-series analysis for real-time prediction and detection of damage to the turbines, ultimately contributing to the efficient management and maintenance of offshore wind turbines.

Jack is a graduate student in Data Science and Artificial Intelligence at the University of Exeter, UK. His research is focused on the application of deep-learning approaches to the downscaling and compression of geospatial data which is of pertinence to the Met Office's defence customers. His work involves contributing to the development of the Met Office's AI weather prediction model, FastNet.

Simon is a graduate student in Data Science and Artificial Intelligence at the University of Exeter, UK. His research focuses on applying machine learning to analyse and emulate regional climate model simulations. By running these climate models, he aims to deepen our understanding of how climate change could impact local and regional environments, helping to provide valuable insights into potential future conditions at finer spatial scales.

Ethan is a graduate student in Computer Science and Mathematics at the University of Exeter, UK. His research centers on using graph neural networks to model spatial variability of earthquake intensity measures, intending to advance emergency response, infrastructure resiliency, and risk mitigation. His research aims at predicting the spatial distribution of the vector of intensity measures using the ground motions recorded through a network of sensors. His approach integrates spatial and temporal modelling of earthquake waves through advanced machine learning to support timely, data-informed decisions that can improve safety and reduce impacts in earthquake-prone areas

Angelo is a graduate student in Computer Science at the University of Exeter, UK. His research focuses on developing a decision-making model for urban water network maintenance, aiming to reduce the economic and environmental impact of avoidable water loss. His interdisciplinary work involves analysing data from predictive models, utilising various deep reinforcement learning techniques to train decision-making agents, and deploying said agents in a system that can be used by non-specialists in a commercial environment to enhance the management and maintenance of water networks.

Lauren completed her PhD in Civil, Environmental, and Geomatic Engineering at University College London (UCL), UK. Her research focused on enhancing the resilience and sustainability of critical infrastructure systems through systems-based, data-driven methods. She developed intelligent, data-driven solutions for each phase of leakage management—anticipation, detection, and restoration—envisioning a self-healing system. These solutions were trained and tested on a dataset of over 2,000 district-metered areas managed by a UK water company. This approach provides a rapid and cost-effective method for identifying potential leaks, offering benefits such as increased infrastructure resilience, optimized repair strategies, and improved consumer confidence, which together promote sustainable demand-side behaviours.