Professor Stephen de Mora CSci CMarSci FRSA FIMarEST is the President of the IMarEST. Educated in chemistry and oceanography, he obtained a BSc from the University College of Swansea and a PhD from UBC, Vancouver. He has taught chemistry, environmental science, and oceanography at universities in the United Kingdom (University of Lancaster), New Zealand (University of Auckland) and Canada (Université de Québec a Rimouski). After a 20 year career in academia, he joined the International Atomic Energy Agency (IAEA) Marine Environment Laboratory in Monaco.
Through the IAEA, he worked with several Regional Seas Programmes and Global Environment Facility (GEF) projects around the world. Steve was awarded a Distinguished Service Award in the year that the IAEA was a co-recipient of the Nobel Prize in 2005. Before returning to the UK to take up the position at Plymouth Marine Laboratory (PML) in 2008, he had established an environmental consultancy based in Canada working for various UN bodies and the World Bank on assignment mostly in Eastern Europe, Western Asia and Central Africa. Steve has retired as the Chief Executive of both the Plymouth Marine Laboratory and PML Applications Ltd and now works as an independent environmental consultant.
Rachel Furner is a data-driven ocean modeller at ECMWF, where she is developing a machine learning-based global ocean model. This model will be used as part of a fully machine-learning based earth system model funded by the Destination Earth initiative.
Rachel has a strong foundation in mathematics and over a decade of experience in ocean modelling, having previously worked at the UK Met Office, contributing to the development and operational use of the NEMO physics-based model for storm surge and coastal ocean forecasting. She recently completed a PhD at the University of Cambridge and the British Antarctic Survey using machine learning to emulate idealised channel ocean configurations.
Ajit Pillai is a Royal Academy of Engineering Research Fellow and Senior Lecturer at the University of Exeter. His research focuses on developing and deploying optimization algorithms and machine learning techniques to support the design and operation of offshore renewable energy systems. Through his ongoing fellowship, he is creating methods that integrate high-fidelity numerical models with targeted, dynamic measurement campaigns using autonomous vessels to improve marine safety and operational decision-making. This work aims to reduce environmental uncertainty and establish a novel spatial data framework for the marine environment.
Ajit has contributed to research projects applying optimization, machine learning, and advanced hydrodynamic modelling to offshore systems. His recent work includes metocean forecasting, offshore wind farm layout optimization, mooring system design, and the use of autonomous platforms for data collection. His research interests lie in combining digital tools and physical modelling to accelerate innovation in offshore renewables.
Ralph is a Professorial Research Fellow in the Department of Statistics at LSE as well as holding a position with The U.S. National Oceanic and Atmospheric Administration. He is a fellow of the Institute for Marine Engineering, Science and Technology, the Society for Underwater Technology, the Marine technology Society, the Royal Meteorological Society and the Royal Society of Arts, Manufactures and Commerce.
Ralph’s academic background is in ocean science and technology. Throughout his career he has worked at the interface between the public and private sectors in meeting the need for monitoring of the ocean environment in support of understanding climate change, the use of ocean resources and the protection of the marine environment. He is a former chair of the IOC/UNESCO Global Ocean Observing System Steering Committee and currently serves on the advisory bodies of a number on leading ocean science research organisations, including Plymouth Marine Laboratory and the Schmidt Ocean Institute. He is Editor in Chief of the Journal of Operational Oceanography and is a founding trustee of the Rising Seas Institute.
Prof. John Siddorn is the Chief Executive of the National Oceanography Centre (NOC), where he leads the organisation’s mission to gain a deeper knowledge of the ocean to address major challenges facing society and the natural world.
Under his leadership, NOC is driving innovation in digital ocean technologies, autonomous systems, and data-driven science for the benefit of society and the planet, positioning the UK at the forefront of global marine research and environmental resilience.
John is the Head of the UK Delegation to the International Oceanographic Commission of UNESCO, an Honorary Fellow at the Met Office and a Fellow of the Alan Turing Institute. He Chairs the interim Executive Committee of Marine Science UK, is on the UN Decade Digital Twins of the Ocean (DITTO) Advisory Board and the Ad Hoc Committee supporting the development of OceanObs ’29.
Before becoming Chief Executive, John served as NOC’s Chief Scientist, where he guided the organisation’s research. Earlier in his career, he held senior roles at the Met Office, including Head of Ocean Forecasting R&D, and began his research journey at Plymouth Marine Laboratory as a biogeochemical modeller.
Dr Jonathan Smith is the Deputy Science Leader of the AI Lab at the British Antarctic Survey (BAS) and leads a team in developing AI systems for decision support in marine planning. The AI decision support systems allows operational planning experts to tap into cutting edge solutions that define ways to coordinate large fleets of ships and autonomous vehicles to maximise data collection whilst minimise carbon cost. On top of this work BAS is developing cutting edge machine learning solutions for marine vessel optimal route planning in changing environmental solutions and forecasting of environmental variables (e.g. Sea-Ice). Prior to joining BAS Jonathan was an invited research fellow at California Institute of Technology, and during this position he developed cutting edge machine learning software for the real-time monitoring of Southern California earthquakes.
