AI and environmental data science research leader specialising in the development and application of artificial intelligence across Earth observation, marine science and environmental research. I lead national AI capability through NEODAAS and institutional AI and data science capability at Plymouth Marine Laboratory, combining scientific research, strategy, infrastructure, researcher development and interdisciplinary collaboration.
My research interests include machine learning for Earth observation, marine computer vision, environmental modelling, ocean biogeochemistry, foundation models, explainable AI and the development of trustworthy and reusable AI methods for environmental science.
I provide scientific and technical leadership in artificial intelligence for marine and environmental science, combining development of national research capability with leadership of interdisciplinary research, infrastructure, researcher training and organisational AI strategy.
I lead artificial intelligence activities within the NERC Earth Observation Data Analysis and AI Service (NEODAAS), supporting the UK environmental research community in the effective application of AI and machine learning to Earth observation. This includes research co-design, specialist technical advice, training, access to scalable computing infrastructure, and the development of reusable and robust AI workflows.
As AI and Data Science Lead at Plymouth Marine Laboratory, I lead the development of organisational AI capability and help set strategic direction for the application of AI across marine and environmental science. My research leadership spans Earth observation, marine computer vision, environmental modelling, ocean biogeochemistry, geospatial foundation models, explainable AI and responsible adoption of emerging AI technologies.
I lead and contribute to collaborative research development across NERC, ESA, EC and other national and international programmes. This includes scientific concept development, interdisciplinary team building, technical design, work-package development, project planning and budgets. I have contributed to more than 20 research proposals with a combined value of approximately £6.8M.
I provide line management, mentoring, postgraduate supervision and technical leadership for researchers working across AI and data science. I develop and deliver specialist AI and Earth-observation training for doctoral researchers, environmental scientists and the wider research community, and contribute to scientific strategy through invited talks, panels, peer review and national and international collaborations.
AI and machine learning: Extensive experience developing, evaluating and applying machine-learning methods across environmental science, Earth observation, marine science, computer vision and signal processing. Expertise includes deep learning, neural networks, computer vision, explainable AI, model evaluation and transferability, and emerging geospatial and foundation-model approaches.
Environmental data science and Earth observation: Experienced in applying AI and data-science methods to satellite Earth observation, ocean colour, marine ecosystems, environmental monitoring and biogeochemical modelling, with a strong focus on scientifically robust and interpretable approaches.
Scientific and technical leadership: Experienced in setting strategic direction for AI capability, leading multidisciplinary research, developing national research capability, and translating scientific requirements into effective and reusable AI solutions.
Research strategy and development: Experienced in scientific concept development, interdisciplinary collaboration, research proposals, work-package development, project planning and the development of new research directions across AI and environmental science.
Research software and scientific computing: Strong scientific programming and research-software background, particularly Python, Linux and Git, alongside experience with C, C++, MATLAB, Bash and LaTeX. Experienced in reproducible scientific workflows, machine-learning frameworks, geospatial computing and high-performance computing.
Research methods: Extensive experience in academic research, experimental design, statistics, model evaluation, scientific publishing, peer review, research software, reproducibility and the critical interpretation of AI-derived results.
People and capability development: Experienced in line management, mentoring, technical leadership, postgraduate supervision, building teams from the ground up and the development of researchers working in AI and data science.
Teaching and knowledge exchange: Associate Fellow of the Higher Education Academy (AFHEA), with experience in undergraduate and postgraduate teaching, doctoral training, professional researcher development, guest lecturing, course design and delivery, and specialist AI and Earth-observation training.
Scientific communication and engagement: Experienced invited speaker, trainer and scientific communicator, with experience communicating complex AI and environmental-science concepts to specialist researchers, students, interdisciplinary collaborators and wider stakeholder audiences.
Audio, signal processing and real-time systems: Research and engineering background in digital signal processing, machine listening, intelligent audio production, acoustics, source separation, sound synthesis and real-time audio systems.
Camila Toledo-Jaime, Jennifer Graham, Ben Hudson, David Moffat, Bryony Townhill and Steven Palmer "Marine heatwave exposure is changing unevenly across Scottish aquaculture sites", Frontiers in Marine Science, 2026. 13:1963854. doi: 10.3389/fmars.2026.1963854
David Moffat, Angus Laurenson, Victor Martinez-Vicente, Gemma Kulk, Xuerong Sun, Robert J. W. Brewin and Shubha Sathyendranath "Beyond chlorophyll: machine learning estimates of diagnostic phytoplankton pigments from multispectral ocean colour data", arXiv:2608.23348 [q-bio.OT], 2026.
Jozef Skakala, Ieuan Higgs, David Moffat "Deep learning model emulators for marine biogeochemistry forecasting from days to decades", arXiv:2606.27168, 2026.
D. Lomeo, S. Simis, M. Warren, D. Moffat, A. Jungblut and E. Tebbs "Mixture Density Networks Improve Inter-Sensor Consistency of Optical Water Type Classification", Transactions on Geoscience and Remote Sensing, 2026.
Skákala, J., Sathyendranath, S., Artioli, Y., Banerjee, D. S., Bouman, H., Brewin, R. J. W., Butenschön, M., Ciavatta, S., Dutkiewicz, S., Fidai, Y., Ford, D., George, G., Guihou, K., Jönsson, B., Bačeković Koloper, M., Kovač, Ž., Krishnakumary, L., Kulk, G., Laufkötter, C., Lessin, G., Mattern, J. P., Melet, A., Mignot, A., Moffat, D., Monteiro, F., Rodriguez Bennadji, M., Rousseaux, C., Swaminathan, R., Ulloa, O., and Tjiputra, J. "Modelling primary production: multitude of theories, or multitude of languages? ", Ocean Science, 3.22; EGUsphere, 2026. 1-39
Chavana-Bryant, C., Wilkes, P., Yang, W., Burt, A., Vines, P., Bennett, A. C., Pickavance, G. C., Cooper, D. L. M., Lewis, S. L., Phillips, O. L., Brede, B., Lau, A., Herold, M., McNicol, I. M., Mitchard, E. T. A., Coomes, D. A., Jackson, T. D., Makaga, L., Milamizokou Napo, H. O., Ngomanda, A., Ntie, S., Medjibe, V., Dimbonda, P., Soenens, L., Daelemans, V., Proux, L., Nilus, R., Labrière, N., Jeffery, K., Burslem, D. F. R. P., Clewley, D., Moffat, D., Qie, L., Bartholomeus, H., Vincent, G., Barbier, N., Derroire, G., Abernethy, K., Scipal, K., and Disney, M. "ForestScan: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data." Earth System Science Data, 18, 1243–1274 February 2026.
Rebecca Millington, Dale Partridge, Helen R. Powley, Gennadi Lessin, David Moffat and Jerry Blackford, "Consistency of clustering analysis of complex 3D ocean datasets." Ecological Informatics
Joseph Smith, Cathryn Birch, John Marsham, and David Moffat, "SII-NowNet: A deep learning framework for nowcasting convection initiation and intensification in the Tropics." Artificial Intelligence for the Earth Systems (AIES)
Abigail Marie Waring, Darren Ghent, David Moffat, Carlos Jimenez and John Remedios, "A Deep Learning Approach to Downscaling Microwave Land Surface Temperatures for a Clear-Sky Merged Infrared-Microwave Product." Remote Sensing 2025, 17(23), 3893
Dawson, G., Vandaele, R., Taylor, A., Moffat, D., Tamura-Wicks, H., Jackson, S., Lickorish, R., Fraccaro, P., Williams, H., Luo, C. and Jones, A., 2025. "A Sentinel-3 foundation model for ocean colour". IEEE Transactions on Geoscience and Remote Sensing
Lucie A. Delobel, David Moffat, Emma Tebbs, Andreas C.W. Baas, "Segmenting and characterising ripple patterns on sand dunes using machine learning and 2D semi-variogram." Remote Sensing of Environment Volume 331 (2025): 115031
D. Sengupta, F. G. Bandao, S. Sathyendranath, G. Kulk, A. Conte, C. Ippolitti, L. Candeloro, D. Moffat, W. Wint, M. Maranesi, R. Scarano, J. Vitorino, G. Brandt and T. M Harish "Sensing human health from space: An assessment of applications and big data platforms." Remote Sensing Applications: Society and Environment 2025
Lauren Biermann, David Moffat, Clive E. Sabel and Thomas E. Stovin "The theoretical role of the wind in aerosolising microplastics and nanoplastics from coastal combined sewer overflows." Nature Scientific Reports 15, 23623. July 2025
Ming-Xi Yang, David Moffat, Yuanxu Dong and Jean-Raymond Bidlot "Deciphering the variability in air-sea gas transfer due to sea state and wind history." PNAS Nexus, pgae389. September 2024
Aser Mata, David Moffat, Sílvia Almeida, Marko Radeta, William Jay, Nigel Mortimer, Katie Awty-Carroll, Oliver R. Thomas, Vanda Brotas, Steve Groom "Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas." Ecological Informatics. 102708, July 2024
Timothy J. Smyth, David Moffat, Glen A. Tarran, Shubha Sathyendranath, François Ribalet and John Casey "Determining drivers of phytoplankton carbon to chlorophyll ratio at Atlantic Basin scale." Frontiers in Marine Science. Volume 10, July 2023
Phil Wilkes, Mathias Disney, John Armston, Harm Bartholomeus, Lisa Bentley, Benjamin Brede, Andrew Burt, Kim Calders, Cecilia Chavana-Bryant, Daniel Clewley, Laura Duncanson, Brieanne Forbes, Sean Krisanski, Yadvinder Malhi, David Moffat, Niall Origo, Alexander Shenkin, Wanxin Yang, "TLS2trees: a scalable tree segmentation pipeline for TLS data." Methods in Ecology and Evolution. Wiley. October 2023.
Satvik Venkatesh, David Moffat, Eduardo Miranda, "Word Embeddings for Automatic Equalization in Audio Mixing." Journal of the Audio Engineering Society, Vol 70 no.9 pp. 753-763; September 2022
David Moffat, Brecht de Man and Joshua D. Reiss, "Semantic Music Production: A Meta-study." Journal of the Audio Engineering Society, vol. 70, no. 7/8, pp. 548-564, July 2022
Satvik Venkatesh, David Moffat, Eduardo Miranda, "You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection." Applied Sciences, 12, 3293. 2022. DOI 10.3390/app12073293
Satvik Venkatesh, David Moffat, Eduardo Miranda, "Investigating the Effects of Training Set Synthesis for Audio Segmentation of Radio Broadcast." Electronics, 2021.
M. Lefford, Gary Bromham, György Fazekas, David Moffat, "Context Aware Intelligent Mixing Systems." Journal of the Audio Engineering Society, 2021.
Marco Martínez, Daniel Stoller, David Moffat, "A Deep Learning Approach to Intelligent Drum Mixing with the Wave-U-Net." Journal of the Audio Engineering Society, 2021.
Thomas Wilmering, David Moffat, Alessia Milo, Mark Sandler, "A History of Audio Effects." Applied Sciences, 2020.
David Moffat, Mark Sandler, "Approaches in Intelligent Music Production." Arts, 2019.
Rod Selfridge, David Moffat, Eldad Avital, Joshua Reiss, "Creating Real-Time Aeroacoustic Sound Effects Using Physically Derived Models." Journal of the Audio Engineering Society, 2018.
David Moffat, Joshua Reiss, "Perceptual Evaluation of Synthesized Sound Effects." ACM Transactions on Applied Perception (TAP), 2018.
Rod Selfridge, David Moffat, Joshua Reiss, "Sound Synthesis of Objects Swinging Through Air Using Physical Models." Applied Sciences, 2017.
Luca Turchet, David Moffat, Ana Tajadura-Jiménez, Joshua Reiss, Tony Stockman, "What do your footsteps sound like? An investigation on interactive footstep sounds adjustment." Applied Acoustics, 2016.
M. Nyssim Lefford, David Moffat and Gary Bromham, "From Intelligent Digital Assistant to Intelligent Digital Collaborator." In Innovation in Music Performance: Technology and Creativity. (pp. 278-291) Editors Jan-Olof Gullö, Russ Hepworth-Sawyer, Justin Paterson, Rob Toulson and Mark Marrington. Focal Press. March 2024
Clive Mead, David Moffat and Gary Bromham, "A History of Distortion." In Distortion in Music Production, The Soul of Sonics. Chapter 1, Editors Gary Bromham and Austin Moore. Focal Press, June 2023.
David Moffat, "AI Music Mixing Systems." In the proceedings of Handbook of Artificial Intelligence for Music, Chapter 13, 2021.
David Moffat, Rod Selfridge, Joshua Reiss, "Sound Effect Synthesis." In the proceedings of Foundations in Sound Design for Interactive Media: A Multidisciplinary Approach, Chapter 13, 2019.
Z. Liu, C. Luo, Z. Fu, and D. Moffat "VBLS: ViT-based Broad Learning System for Remote Sensing Scene Classification" In the proceedings of IEEE 2026 International Geoscience and Remote Sensing Symposium, 2026. pp. 180-184
Di Campli San Vito, P., Brewster, S., Venkatesh, S., Miranda, E., Kirke, A., Moffat, D., Banerjee, S., Street, A., Fachner, J. and Odell-Miller "RadioMe: Supporting Individuals with Dementia in Their Own Home... and Beyond?" In the proceedings of 2022 CHI Conference on Human Factors in Computing Systems (CHI 22) Workshop 32, New Orleans, LA, USA, 30 Apr 2022.
Satvik Venkatesh, David Moffat, Alexis Kirke, Gözel Shakeri, Stephen Brewster, Jörg Fachner, Helen Odell-Miller, Alex Street, Nicolas Farina, Sube Banerjee, Eduardo Miranda, "Artificially synthesising data for audio classification and segmentation to improve speech and music detection in radio broadcast." In the proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.
Shakeri Gozel, Stephen Brewster, Satvik Venkatesh, David Moffat, Alexis Kirke, Eduardo Miranda, Sube Banerjee, Alex Street, Jörg Fachner, Helen Odell-Miller, "RadioMe: Challenges During the Development of a Real Time Tool to Support People With Dementia." In the proceedings of Conference on Human Factors in Computing Systems (CHI), 2021.
Clive Mead, David Moffat, Eduardo Miranda, "Composing, Recording and Producing with Historical Equipment and Instrument Models." In the proceedings of Audio Engineering Society Convention 139, 2020.
M. Lefford, Gary Bromham, David Moffat, "Mixing with Intelligent Mixing Systems: Evolving Practices and Lessons from Computer Assisted Design." In the proceedings of Audio Engineering Society Convention 148, 2020.
Gary Bromham, David Moffat, Mathieu Barthet, György Fazekas, "Retro in Digital: Understanding the Semantics of Audio Effects." In the proceedings of DMRN+14: Digital Music Reserach Network One-Day Workshop, 2019.
Satvik Venkatesh, David Moffat, Eduardo Miranda, "RadioMe: Artificially Intelligent Radio for People with Dementia." In the proceedings of DMRN+14: Digital Music Research Network One-Day Workshop, 2019.
David Moffat, Mark Sandler, "Machine Learning Multitrack Gain Mixing of Drums." In the proceedings of Audio Engineering Society Convention 147, 2019.
David Moffat, Mark Sandler, "An Automated Approach to the Application of Reverberation." In the proceedings of Audio Engineering Society Convention 147, 2019.
Gary Bromham, David Moffat, Mathieu Barthet, Anne Danielsen, György Fazekas, "The Impact of Audio Effects Processing on the Perception of Brightness and Warmth." In the proceedings of ACM Audio Mostly Conference, 2019.
David Moffat, Mark Sandler, "Automatic Mixing Level Balancing Enhanced through Source Interference Identification." In the proceedings of Audio Engineering Society Convention 146, 2019.
Gary Bromham, David Moffat, György Fazekas, Mathieu Barthet, Mark Sandler, "The impact of compressor ballistics on the perceived style of music." In the proceedings of Audio Engineering Society Convention 145, 2018.
David Moffat, Mark Sandler, "Adaptive Ballistics Control of Dynamic Range Compression for Percussive Tracks." In the proceedings of Audio Engineering Society Convention 145, 2018.
David Moffat, Florian Thalmann, Mark Sandler, "Towards a Semantic Web Representation and Application of Audio Mixing Rules." In the proceedings of Proceedings of the 4th Workshop on Intelligent Music Production (WIMP), 2018.
David Moffat, Joshusa Reiss, "Objective Evaluations of Synthesised Environmental Sounds." In the proceedings of Proceedings of the 21th International Conference on Digital Audio Effects (DAFx-18), 2018.
David Moffat, David Ronan, Joshusa Reiss, "Unsupervised Taxonomy of Sound Effects." In the proceedings of Proc. 20th International Conference on Digital Audio Effects (DAFx-17), 2017.
Rod Selfridge, David Moffat, Joshua Reiss, "Physically Derived Sound Synthesis Model of a Propeller." In the proceedings of ACM Audio Mostly Conference, 2017.
Rod Selfridge, David Moffat, Joshua Reiss, Eldad Avital, "Real-Time Physical Model for an Aeolian Harp." In the proceedings of Proceedings of the 24th International Congress on Sound and Vibration, 2017.
Rod Selfridge, David Moffat, Joshua Reiss, "Real-Time Physical Model for Synthesis of Sword Swing Sounds." In the proceedings of Proceedings of the 14th International Conference on Sound and Music Computing (SMC), 2017.
David Moffat, Joshua Reiss, "Perceptual evaluation of synthesised sound effects." In the proceedings of DMRN+11: Digital Music Research Network Workshop, 2016.
Brecht De, Nicholas Jillings, David Moffat, Joshua Reiss, Ryan Stables, "Subjective comparison of music production practices using the Web Audio Evaluation Tool." In the proceedings of 2nd AES Workshop on Intelligent Music Production, 2016.
Nicholas Jillings, Brecht De, David Moffat, Joshua Reiss, "Web Audio Evaluation Tool: A framework for subjective assessment of audio." In the proceedings of Proceedings of the 2nd Web Audio Conference, 2016.
Lucas Mengual, David Moffat, Joshua Reiss, "Modal Synthesis of Weapon Sounds." In the proceedings of Proceedings of the Audio Engineering Society Conference: 61st International Conference: Audio for Games, 2016.
David Moffat, Joshua Reiss, "Implementation and Assessment of Joint Source Separation and Dereverberation." In the proceedings of Proceedings of the Audio Engineering Society Conference: 60th International Conference: DREAMS (Dereverberation and Reverberation of Audio, Music, and Speech), 2016.
David Ronan, David Moffat, Hatice Gunes, Joshua Reiss, "Automatic Subgrouping of Multitrack Audio." In the proceedings of Proceedings of the 18th International Conference on Digital Audio Effects (DAFx-15), 2015.
David Moffat, David Ronan, Joshusa Reiss, "An Evaluation of Audio Feature Extraction Toolboxes." In the proceedings of Proceedings of the 18th International Conference on Digital Audio Effects (DAFx-15), 2015.
Nicholas Jillings, Brecht De, David Moffat, Joshua Reiss, "Web Audio Evaluation Tool: A Browser-Based Listening Test Environment." In the proceedings of Proceedings of the Sound and Music Computing Conference, 2015.
Invited Seminar at NCEO Science Seminar Series, Online
Panel Chair and Presentation at UK Earth Observation Conference, University of Warwick, Warwick, UK
Invited Talk at British Antarctic Survey, Cambridge, UK
Conference Talk at Ocean Sciences Meeting, Glasgow, UK
Invited Seminar at Energy and Environment Group (EEG), University of Cambridge, Cambridge, UK
Invited Plenary Talk at Association for the Sciences of Limnology and Oceanography (ASLO) Conference, Charlotte, North Carolina, USA
Keynote Talk at Machine Learning for Earth Observation (ML4EO) Conference, University of Exeter, Exeter, UK
A complete record of my public speaking and talks is available on my Talks page.
I have extensive experience in university teaching, postgraduate research supervision, doctoral training and professional researcher development. I am an Associate Fellow of the Higher Education Academy (AFHEA) and have designed and delivered teaching from undergraduate level through to specialist training for doctoral researchers and established scientists.
I regularly develop and deliver specialist training in artificial intelligence, machine learning and Earth observation. This has included courses and workshops for NEODAAS and the Field Spectroscopy Facility (FSF), NCEO, EUMETSAT, the Trevor Platt Science Foundation, Machine Learning for Earth observation (ML4EO) conference and other national and international research communities.
My training combines the principles of machine learning with practical scientific applications, including preparing Earth observation data for AI, classification and regression, neural networks, computer vision, model evaluation and the development of robust machine-learning workflows.
A complete record of my teaching, researcher training and course delivery is available on my Teaching and Training page.
I have developed and delivered postgraduate training for doctoral researchers, including the SENSE Centre for Doctoral Training and the University of Exeter Environmental Intelligence CDT. Training has covered machine learning for Earth observation, environmental data science, AI workflows, research methods and practical scientific computing.
I have taught undergraduate and postgraduate material across computer science, artificial intelligence, programming, audio engineering, digital signal processing and research methods.
At the University of Plymouth, I served as Programme Leader for the BSc Computing, Audio and Music Technology programme, with responsibilities spanning teaching, curriculum delivery, student support, programme administration and recruitment.
Previously, at Queen Mary University of London, I contributed to teaching in sound recording and production, digital audio, sound design and electronic engineering.
I supervise postgraduate research applying artificial intelligence, machine learning and computational methods across environmental science, Earth observation and interdisciplinary applications.
Current PhD supervision
Completed PhD supervision
Completed ResM Supervision
Ned Felnor, University of Plymouth, Second Supervisor, completed 2021
Clive Mead