Dani Jones

Associate Research Scientist

4840 S. State Rd.
Ann Arbor, MI 48108-9719

dannes@umich.edu

Google Scholar Profile

Researcher Profile:

Dani Jones’s research program drives CIGLR’s portfolio of research in data science, machine learning, and artificial intelligence, as applied to physical limnology, weather forecasting, water cycle predictions, ecology, and observing system design. This research program aims to advance societal adaptations to the effects of climate change, including flooding of coasts, rivers, and cities. Dani’s background is in physical oceanography, with specific expertise in adjoint modeling for comprehensive sensitivity analysis and unsupervised classification for data analysis, mostly applied to the North Atlantic and Southern Ocean. In Dani’s current role, they are establishing CIGLR’s new Artificial Intelligence Laboratory, leveraging the institute’s extensive observing assets, datasets, modeling capacity, interdisciplinary expertise, and numerous regional and international partnerships.

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)
Research Interest/Area of Expertise:
    • Physical oceanography: large-scale circulation and dynamics
    • Numerical modeling: adjoint modeling for sensitivity analysis
    • Machine learning: unsupervised classification, observing system design
Recent Publications: 

Jones, D.C., Sonnewald, M., Zhou, S., Hausmann, U., Meijers, A.J.S., Rosso, I., Boehme, L., Meredith, M.P., & Naveira Garabato, A.C. (2023). Unsupervised classification identifies coherent thermohaline structures in the Weddell Gyre region. Ocean Science. 19, 857-885. (DOI:10.5194/os-19-857-2023). [Altmetric Score]

McMonigal, K., Evans, N., Jones, D., Brett, J., James, R. C., Arroyo, M. C., et al. (2023). Navigating gender at sea. AGU Advances. 4, e2023AV000927. (DOI:10.1029/2023AV000927). [Altmetric Score

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). [Altmetric Score]


Recent Presentations:

Thomas, S., Jones, D., Mayo, T., and Gopinathan, D. (2023): Tropical cyclone storm surge emulation around New Orleans, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13337.

Jones, D., Sonnewald, M., Rosso, I., Zhou, S., and Boehme, L. (2022): Unsupervised classification identifies coherent thermohaline structures in the Weddell Gyre, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10528.