about

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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.

goals

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.

team

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

Lead

Inria Chile Research Center

Artificial Intelligence

Luis Martí

Co-lead

Inria Chile Research Center

Machine learning & Evolutionary Computing

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, study, optimisation & monitoring of artificial ecosystems

Michèle Sebag

Team coordinator

TAU project team - Inria Saclay

Machine learning applications to social sciences

Marc Schoenauer

Team coordinator

TAU project team - Inria Saclay

Evolutionary Computation

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)

Macroecology

André Abreu

Team coordinator

Fondation Tara Océans

International Relations & Economic Development

Colomban de Vargas

Team coordinator

GO-SEE CNRS Federation

Marine biologist

Damien Eveillard

Team coordinator

ComBi - Nantes University

System Biologist & Oceanographer

Researchers

Walid Djema

Researcher

BIOCORE project team - Inria Sophia-Antipolis

Control heory applied to biological and medical systems

Ana Muñoz

Researcher

Inria Chile Research Center

Data Governance

Dante Travisany

Researcher

Inria Chile Research Center

Bioinformatics, Complex Systems, Genomics & Machine Learning

Hugo Carrillo

Researcher

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 & Software architecture

Astrid Reyes De La Rosa

Engineer

Inria Chile Research Center

Software engineering

Nicolás Aguilera

Engineer

Inria Chile Research Center

Software engineering

Publications

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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) at NeurIPS 2020. slides view online 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

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Open positions in Chile

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

Open positions in France