Directorate

Gordon is a Distinguished Professor of Distributed Systems in the School of Computing and Communications, Lancaster University, and is Co-Director of CEEDS. He holds a prestigious EPSRC 5-year Senior Fellowship in Digital Technology and Living with Environmental Change, looking at how computer science can support environmental scientists in understanding the complexities of the natural environment, including new sources of data, new ways of storing and processing data, and new methods of making sense of such data. He also has a strong interest in the design and implementation of virtual labs in the cloud, offering collaborative environments for data-driven environmental science.

Paula is a Co-Director of CEEDS, Professor of Land and Water Modelling and Principal Natural Capital Scientist at the UK Centre for Ecology & Hydrology in Lancaster. Her research focuses on the design and implementation of integrated models that link together multiple land and water-related sectoral models to assess interdependencies, synergies and trade-offs. She uses different types of simulation models (e.g. process-based models, agent-based models, statistical models and emulators) to investigate the impacts of climate and socio-economic change and explore the sustainable management of complex socio-ecological systems. Her interests include investigating uncertainties associated with scenario and model development and application, including attribution and propagation of uncertainty in integrated modelling.

Mal is a Co-Director of CEEDS, Reader in Environmental Sensing at the Lancaster Environment Centre, and a Principle Investigator for the UK Centre for Polar Observation & Modelling. His research focuses on new satellite mission concepts and data processing, and the extent to which they can transform our understanding of the response of ice sheets to climate change. With the ever-increasing volumes and complexity of satellite data, he is interested in how the closer integration of Environmental and Data Science can fully unlock the potential that these data hold.