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Workshop
AIMOCC -- AI: Modeling Oceans and Climate Change
Luis Martí · Nayat Sánchez-Pi





Oceans play a key role in the biosphere, regulating the carbon cycle; absorbing emitted CO2 through the biological pump, and a large part of the heat that the remaining CO2 and other greenhouse gases retained in the atmosphere. Understanding the drivers of micro and macroorganisms in the ocean is of paramount importance to understand the functioning of ecosystems and the efficiency of the biological pump in sequestering carbon and thus abating climate change.

AI, ML, and mathematical modeling tools are key to understanding oceans and climate change. Consequently, the topics of interest of this workshop can be grouped into two sets.
In regard to AI and modeling, the main focus is set on:
- handling of graph-structured information,
- ML methods to learn in small data contexts,
- causal relations, interpretability, and explainability in AI,
- integrating model-driven and data-driven approaches, and
- to develop, calibrate, and validate existing mechanistic models.

In the domain application area, the main questions to be addressed are:
- Which are the major patterns in plankton taxa and functional diversity?
- How these patterns and drivers will likely change under climate change?
- How will changes affect the capacity of ocean ecosystems to sequester carbon from the atmosphere?
- What relations bind communities and local conditions?
- What are the links between biodiversity functioning and structure?
- How modern AI and computer vision can be applied as research and discovery support tool to understand planktonic communities?
- How new knowledge can be derived from anomaly detection, causal learning, and explainable AI.

The goal of this workshop is to bring together researchers that are interested and/or applying AI and ML techniques to problems related to marine biology, modeling, and climate change mitigation. We also expect to attract natural science researchers interested in learning about and applying modern AI and ML methods.