There is strong scientific evidence on the adverse effects of climate change on the global ocean. These changes will have a drastic impact on almost all life forms in the oceans with further consequences on food security, the ecosystems in coastal and inland communities. Despite these impacts, scientific data and infrastructures are still lacking to understand better and quantify the consequence of these perturbations on the marine ecosystem. It is necessary not only to gather more data but also to develop and apply state-of-the-art mechanisms capable of turning this data into effective knowledge, policies, and action. This is where artificial intelligence, machine learning, and modeling tools are called for.

This Inria Challenge OcéanIA aims at developing new artificial intelligence and mathematical modeling tools to contribute to the understanding of the structure, functioning, underlying mechanisms, and dynamics of the oceans and their role in regulating and sustaining the biosphere, and tackling the climate change. OcéanIA is then an opportunity to structure Inria’s contributions around a global scientific challenge in the convergence of Artificial Intelligence, Biodiversity & Climate Change.

OcéanIA is a four-years project (11.2020–10.2024) involving Inria teams in Chile, Paris, Saclay, and Sophia-Antipolis, and the Fondation Tara Océans, the Center of Mathematical Modeling (CMM, U.Chile), the Pontificia Universidad Católica de Chile (PUC), the GO-SEE CNRS Federation, and the Laboratoire des Sciences du Numérique de Nantes (LS2N). See the full description of the team here.


The goals of the project are structured in two directions. One that gathers the work from computer science and applied math to meet the challenges of the problem. The other focuses on applying the results of the first in multi-disciplinary application contexts.

Computer science and math objectives

Data governance, curation, and availability Data governance policy for marine biology and oceanographic data, consolidated access to data, and scientific computing software stacks.
Structured and graph-based neural networks Model depth, models scalability, graph topological heterogeneity, and dynamic graphs.
Learning and adaptation in small data contexts Transfer learning and domain adaptation, active and few-shot learning, and multi-source and multi-task learning deep neural models.
Causality and explainable models in AI Causal inference, explainable AI and adversarial machine learning, interpretable shadow models and causal inference for understanding internal representations.
Model-driven and data-driven integration and hybrids Learning PDEs from data, understanding neural network learning dynamics, and hybrid models combining PDE solvers and neural networks.
Development, calibration, and validation of mechanistic models Identifiability issues, metabolic model reduction, and Navier-Stokes equations: From Eulerian to Lagrangian.

Multi-disciplinary applied objectives

Biodiversity and ecosystem functioning Meta-metabolic modeling, phytoplankton biodiversity concerning temperature, present, and future, Data assimilation in biogeochemical models: Predicting the future.
Understanding plankton communities using AI, ML, and vision Plankton identification from satellite images, connecting images and genomic features, anomaly detection, and explainable AI for automatic plankton discovery.

Interrelation of the objectives.


The OcéanIA team has diverse combination of skills, experience, and interests, something that is necessary to address a research-intensive and multi-disciplinary project such as this one.

Scientific Comittee

Nayat Sánchez-Pi


Inria Chile Research Center

Artificial Intelligence

Luis Martí


Inria Chile Research Center

Machine learning

Julien Salomon

Team coordinator

ANGE project team - Inria Paris

Applied mathematics

Jacques Sainte-Marie

Team coordinator

ANGE project team - Inria Paris

Senior scientist

Olivier Bernard

Team coordinator

BIOCORE project team - Inria Sophia-Antipolis

Modelling, optimisation and monitoring of artificial ecosystems

Michèle Sebag

Team coordinator

TAU project team - Inria Saclay

Machine learning

Marc Schoenauer

Team coordinator

TAU project team - Inria Saclay

Machine learning

Alejandro Maass

Team coordinator

Center of Mathematical Modeling (CMM) - Universidad de Chile

Ergodic theory and systems biology

Pablo Marquet

Team coordinator

Pontificia Universidad Católica de Chile (PUC)


André Abreu

Team coordinator

Fondation Tara Océans

International relations and economic development

Colomban de Vargas

Team coordinator

GO-SEE CNRS Federation

Marine biologist

Damien Eveillard

Team coordinator

ComBi - Nantes University

System biologiy and oceanography


Walid Djema


BIOCORE project team - Inria Sophia-Antipolis

Control heory applied to biological and medical systems

Ana Muñoz


Inria Chile Research Center

Data governance

Dante Travisany


Inria Chile Research Center

Bioinformatics, complex systems, genomics and machine learning

Hugo Carrillo


Inria Chile Research Center

Inverse problems, numerical analysis, mathematical modeling and simulation

Engineering team

Andrés Vignaga

Engineering team coordinator

Inria Chile Research Center

Software engineering and architecture

Nicolás Aguilera


Inria Chile Research Center

Software engineering



Keynotes and talks

  1. Sánchez-Pi, N. (2020). OcéanIA: AI, Oceans and Climate Change. In A. Ruiz-Garcia, I. Arraut, J. M. Banda, I. Lopez-Francos, F. Latorre, P. Magalhães Braga, K. Caballero Barajas, S. H. Garrido Mejia, E. U. Moya-Sánchez, V. Fernandes Caridá, C. Miranda, G. Bejarano, & J. Ortega Caro (Eds.), LatinX in AI (LXAI) workshop at NeurIPS 2020. slides view online bibtex

Journal Articles

  1. Demory, D., Weitz, J. S., Baudoux, A.-C., Touzeau, S., Simon, N., Rabouille, S., Sciandra, A., & Bernard, O. (2021). A thermal trade-off between viral production and degradation drives virus-phytoplankton population dynamics. Ecology Letters, 24(6), 1133–1144. doi: 10.1111/ele.13722 abstract bibtex

Conference papers

  1. Sánchez-Pi, N., Martí, L., Abreu, A., Bernard, O., de Vargas, C., Eveillard, D., Maass, A., Marquet, P. A., Sainte-Marie, J., Salomon, J., Schoenauer, M., & Sebag, M. (2020). Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change. In D. Dao, E. Sherwin, P. Donti, L. Kaack, L. Kuntz, Y. Yusuf, D. Rolnick, C. Nakalembe, C. Monteleoni, & Y. Bengio (Eds.), Tackling Climate Change with Machine Learning workshop at NeurIPS 2020. paper slides abstract bibtex


AI: Modeling Oceans and Climate Change

An ICLR 2021 Workshop

It is our distinct pleasure to invite you to the AI: Modeling Oceans and Climate Change (AIMOCC 2021) Workshop to be held in conjunction with the Ninth International Conference on Learning Representations (ICLR 2021) and hosted in virtual-only mode.

The Anthropocene has brought along a drastic impact on almost all life forms on the planet. Considering the importance and amount of water in this speck of dust in the middle of nowhere that we inhabit, we should have called it Planet Ocean. Oceans are not only important because of their volume but are also about the functions and contributions they provide to biodiversity, the human species included.

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. Consequently, the workshop will be a first stone on building a multi-disciplinary community behind this research topic, with collaborating researchers that share problems, insights, code, data, benchmarks, training pipelines, etc. Together, we aim to ultimately address an urgent matter regarding the future of humankind, nature, and our planet.

Workshop programme

The workshop will take place on Friday, 7 May 2021. A zoom link will be shared to allow the participation anyone insterested.

  • Papers and presentations will be made available ASAP. Please note that programme times are in the CLT/EST (UTC-5h)
    • PST (UTC-8h): start at 06:00 (-3h).
    • CET (UTC+1h): start at 15:00 (+6h).
    • NST (UTC+8h): start at 23:00 (+14h).

Detailed programme (CLT/EST timezone)

  • 09:00 - 09:05. Opening comments and welcome by the organizers.

  • 09:05 - 09:45. Keynote presentation: Jacques Sainte-Marie, ANGE Team (Inria Paris and Sorbonne Université).

  • 09:45 - 10:05. Investigating Ground-level Ozone Formation: A Case Study in Taiwan. Yu-Wen Chen (Academia Sinica), Sourav Medya (Northwestern University), and Yi-Chun Chen (Academia Sinica). abstract paper (pdf)
  • 10:05 - 10:25. Model Discovery in the Sparse Sampling Regime. Gert-Jan Both, Georges Tod, and Remy Kusters (CRI). abstract paper (pdf)
  • 10:25 - 10:45. Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization. Björn Lütjens (MIT), Brandon Leshchinskiy (MIT), Christian Requena-Mesa (Computer Vision Group, Friedrich Schiller University Jena; DLR Institute of Data Science, Jena; Max Planck Institute for Biogeochemistry, Jena), Farrukh Chishtie (Spatial Informatics Group), Natalia Diaz Rodriguez (ENSTA Paris and INRIA Flowers), Oceane Boulais (NOAA), Aruna Sankaranarayanan (MIT), Aaron Piña (NASA Headquarters), Yarin Gal (University of Oxford), Chedy Raissi (INRIA), Alexander Lavin (Institute for Simulation Intelligence), and Dava Newman (MIT). abstract paper (pdf)

  • 10:45 - 11:05. Coffee break and short paper discussions (on gather.town: https://gather.town/app/47bitsZ0TGMpV7Ul/testing-space).
  • Short papers:
    • PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling. Björn Lütjens (MIT), Mark Veillette (MIT Lincoln Laboratory), Dava Newman (MIT), and Cait Crawford (IBM). abstract
    • Generative modeling of spatio-temporal weather patterns with extreme event conditioning. Konstantin Klemmer (University of Warwick), Sudipan Saha (Technical University of Munich), Matthias Kahl (Technical University of Munich), Tianlin Xu (London School of Economics and Political Science), and Xiaoxiang Zhu (Technical University of Munich). abstract paper (pdf)
    • CropGym: A reinforcement learning environment for crop management. Hiske Overweg, Herman Berghuijs, and Ioannis N. Athanasiadis (Wageningen University and Research). abstract paper (pdf)
    • Frost Forecasting Model using Graph Neural Networks with Spatio-Temporal Attention Hernán Lira, Luis Martí, and Nayat Sanchez-Pi (Inria Chile Research Center). abstract paper (pdf)
  • 11:05 - 11:45. Keynote presentation: Daniele Iudicone (Stazione Zoologica Anton Dohrn).

  • 11:45 - 12:05. Feature Importance in a Deep Learning Climate Emulator. Wei Xu (Brookhaven National Laboratory), Xihaier Luo (Brookhaven National Laboratory), Yihui (Ray) Ren (Brookhaven National Laboratory), Ji Hwan Park (Brookhaven National Laboratory), Shinjae Yoo (Brookhaven National Laboratory), and Balu Nadiga (Los Alamos National Lab). abstract paper (pdf)
  • 12:05 - 12:25. Assessing Physics Informed Neural Networks in Ocean Modelling and Climate Change Applications Taco de Wolff, Hugo Carrillo Lincopi, Luis Martí, and Nayat Sanchez-Pi (Inria Chile Research Center). abstract paper (pdf)
  • 12:25 - 12:45. Deep Embedded Clustering for BioAcoustic Clustering of Marine Mammal Vocalization. Ali Jahangirnezhad (University of Washington Bothell) and Afra Mashhadi (University of Washington). abstract paper (pdf)

  • 12:45 - 13:20. Keynote presentation: Michèle Sebag, TAU Team (LISN, Inria, CNRS, and Univ. Paris Saclay).

  • 13:20 - 13:30. Final remarks and open discussion.


We welcome submissions of long (8 pages) full papers and short (4 pages) summary papers. To prepare your submission, please use the ICLR 2021 LaTeX style files provided at: https://github.com/ICLR/Master-Template. Use the following link to submit your proposal(s): AIMOCC 2021 CMT submission site.

Important dates

  • [Updated] Submission deadline : April 12, 2021 (UTC-12).
  • Notification of acceptance: April 19, 2021.
  • [Updated] Reception of final version: May 5, 2021.


Topics of interest of this workshop can be grouped into two sets:

  1. Addressing and advancing the state of the art in areas like AI, ML, mathematical modeling and simulation. Here the focus is set on:
  • improving neural network handling of graph-structured information,
  • improving the capacity of ML methods to learn in small data contexts,
  • understanding causal relations, interpretability and explainability in AI,
  • integrating model-driven and data-driven approaches, and
  • to develop, calibrate, and validate existing mechanistic models.
  1. Focus on answering the questions from the application domain, where the main questions to be addressed are:
  • Which are the major patterns in plankton taxa and functional diversity?
  • Which are the major drivers of patterns and how do they interact?
  • How these patterns and drivers will likely change under climate change?
  • How will these changes affect the capacity of ocean ecosystems to sequester carbon from the atmosphere, that is the biological carbon pump?
  • 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 tools to understand planktonic communities?
  • How new biological knowledge can be derived from the application of anomaly detection, causal learning, and explainable AI.


  • Nayat Sánchez-Pi and Luis Martí, Inria Chile.

Scientific committee

  • José Manuel Molina, Universidad Carlos III de Madrid,
  • Julien Salomon and Jacques Sainte-Marie, Inria Paris,
  • Olivier Bernard, Inria Sophia-Antipolis,
  • Michèle Sebag and Marc Schoenauer, Inria Saclay,
  • Alejandro Maass, Center of Mathematical Modeling (CMM), Universidad de Chile,
  • Pablo Marquet, Pontificia Universidad Católica de Chile (PUC),
  • André Abreu, Fondation TARA Océan,
  • Ana Cristina Garcia, Unirio - Federal University of Rio de Janeiro State,
  • Hernán Lira, Inria Chile Research Center,
  • Hugo Carrillo Lincopi, Inria Chile Research Center,
  • Leandro Fernandes, Universidade Federal Fluminense,
  • Roberto Santana, University of the Basque Country (UPV/EHU),
  • Colomban De Vargas, GO-SEE CNRS Federation, and
  • Damien Eveillard, ComBi, Université de Nantes.

Diversity commitment

We will seek diversity in all aspects, both in school of thought, nationalities, stages in the academic career, etc.


We will publish the accepted papers and talk abstracts (before the event) and the slides of the speakers (after the event) on the workshop website. We will include a bibliography of most relevant research papers to facilitate cross pollination of ideas between these fields. Similarly, we will record the workshop and publish it online.




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Open positions in Chile

Additional information and other positions are listed in the Inria Chile website.

Open positions in France

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