Image
back to all faculty
Dani Jones
Associate Research Scientist
Research Faculty Member
Geospatial Data Sciences
Cross-cutting
Office
4040 Dana and NOAA GLERL
Lab website
Websites
About
My research program focuses on CIGLR’s portfolio of work in data science, machine learning, and artificial intelligence as applied to physical limnology, weather forecasting, water cycle predictions, and observing system design. The overarching goal of this program is to advance societal adaptations to the effects of climate change, including flooding of coasts, rivers, and cities. My background lies in physical oceanography, with specific expertise in adjoint modeling for comprehensive sensitivity analysis and unsupervised classification for data analysis, primarily applied to the North Atlantic and Southern Ocean. In my current role, I am establishing CIGLR’s new Artificial Intelligence Laboratory (CIGLR AI Lab), leveraging the institute’s extensive observing assets, datasets, modeling capacity, interdisciplinary expertise, and numerous regional and international partnerships.
Publications
Jones, D., and Coauthors (2025). Mapping Out How Machine Learning and Artificial Intelligence Will Change Great Lakes Observations, Modeling, and Forecasting in the Coming Decade. Bulletin of the American Meteorological Society, 106, E378–E385, DOI:10.1175/BAMS-D-24-0304.1 (https://doi.org/10.1175/BAMS-D-24-0304.1)
Yao, L., Taylor, J. R., Jones, D. C., & Bachman, S. D. (2025). Identifying Ocean Submesoscale Activity from Vertical Density Profiles Using Machine Learning. Earth and Space Science, 12, e2022EA002618, DOI:10.1029/2022EA002618 (https://doi.org/10.1029/2022EA002618)
Pimm, C., Williams, R. G., Jones, D., and Meijers, A.J.S. (2024). Surface heat fluxes drive a two-phase response in Southern Ocean mode water stratification. Journal of Geophysical Research: Oceans, 129, e2023JC020795, DOI:10.1029/2023JC020795 (https://doi.org/10.1029/2023JC020795)
Furner, R., Haynes, P., Jones, D.C., Munday, D., Paige, B., Shuckburgh, E. (2024). The challenge of land in a neural network ocean model. Environmental Data Science, 3, e40, DOI:10.1017/eds.2024.49 (https://doi.org/10.1017/eds.2024.49)
Andersson, T., W.P. Bruinsma, S. Markou, J. Requeima, A. Coca-Castro, A. Vaughan, A. Ellis, M.A. Lazzara, D. Jones, J.S. Hosking, and R.E. Turner (2023). Environmental sensor placement with convolutional Gaussian neural processes. Environmental Data Science, 2, E32, DOI:10.1017/eds.2023.22 (https://www.cambridge.org/core/journals/environmental-data-science/article/environmental-sensor-placement-with-convolutional-gaussian-neural-processes/F466DBCE3FA1088E04335D98225FA572)
Research
- Machine learning: predictive modeling, unsupervised classification, observing system design
- Physical oceanography: large-scale circulation and dynamics
- Numerical modeling: adjoint modeling for sensitivity analysis
Accolades
Laws Prize, British Antarctic Survey, Cambridge, UK (2021)
UKRI Future Leaders Fellowship (2020 - 2023)
Education
Ph.D. in Atmospheric Science (Oceanography), Colorado State University (2013)
M.S. in Mathematics, Georgia Southern University (2009)
M.S. in Physics, University of Kentucky (2007)
B.S. in Physics, Georgia Southern University (2005)
Affiliations
Co-chair, Observing System Design Capability Working Group, Southern Ocean Observing System [SOOS] (https://soos.aq/activities/cwg/osd)
Affiliate Faculty, Department of Mathematical Sciences, Georgia Southern University
Honorary Researcher, British Antarctic Survey, Cambridge, UK
In the Media