Frank is a PhD student in Computer Science at the University of Exeter, UK. His research focuses on infrastructural surrogate modelling based on interpretable and process-informed deep learning modelling. His aim is to develop machine learning based solutions for improving community resiliency and sustainablity in a user-informed manner.

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.

Alfie is a graduate student in Data Science and Artificial Intelligence at the University of Exeter, UK. His research focuses on developing process-informed artificial-neural-networks-based generative models to simulate response spectra of earthquake ground motions. By incorporating key seismic features such as earthquake magnitude, epicentral distance, and site's soil shear-wave velocity, his work aims to generate realistic ground motions that could enhance earthquake early warning systems and seismic risk assessment.

Yu is a graduate student in Data Science at the University of Exeter, UK. Her research focuses on developing a graph neural network (GNN)-based decision support system for the management of water distribution systems, and her research aims to develop GNNs for modelling spatio-temporal dynamics of water distribution networks. Through this data-driven and deep-learning based approach, she seeks to improve the reliability, efficiency, and sustainability of water systems by reducing water losses, energy utilization, and maintenance costs.

Anjana is a graduate student in Advanced Computer Science at the University of Exeter, UK. Her research, in collaboration with Rothamsted Research, focuses on developing an AI-driven model to predict crop yield by integrating datasets on soil moisture, weather, management practices, and satellite data. The model aims to improve yield forecasts and assess the impact of climate extremes on agricultural productivity.

Zak is a graduate student in Data Science at the University of Exeter, UK. His research focuses on developing a predictive model for water demand, comparing both temporal and spatial modelling approaches. By utilizing real-time data and historical patterns, he incorporates factors like seasonal variations whilst leveraging Bayesian statistics and machine learning techniques with the aim of enhancing forecasts of water flow demands to ultimately support better water resource management in communities.

Ben is a graduate student in Digital Technology Solutions (DTS) at the University of Exeter, UK. His research focuses on developing deep learning architectures to model flow rates and predict leakages in UK water distribution networks. His work aims to advance the development of more sustainable, autonomous water systems, reducing water loss and improving overall efficiency in distribution 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.

Martin completed his MSc in Data Science at the University of Exeter, UK. His research focused on time-series modelling and machine learning-based predictive modelling for water distribution management. His area of interest included 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 completed his MSc in Data Science at the University of Exeter, UK. His research centered on the structural health monitoring of offshore wind turbine blades through deep learning modelling. By leveraging convolutional, recurrent, and transformer neural networks, Jay developed advanced neural network architectures that enhance the reliability and efficiency of wind energy systems. His interdisciplinary work involved 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.

Simon completed his MSc in Data Science at the University of Exeter, UK. His research focused on applying machine learning to analyse and emulate regional climate model simulations. He utilized these climate models 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.

Jack completed his MSc in Data Science at the University of Exeter, UK. His research 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 current work involved contributing to the development of the Met Office's AI weather prediction model, FastNet.