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 Océan, 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.

The goals are discussed in depth in the project white book:

  • Sanchez-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. (2021). OcéanIA: AI, Data, and Models for Understanding the Ocean and Climate Change. (N. Sanchez-Pi & L. Martí, Eds.). Lille/Paris/Saclay/Santiago/Sophia-Antipolis: Inria – Institut national de recherche en sciences et technologies du numérique. download pdf

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 Committee

Nayat Sánchez-Pi

Lead

Inria Chile Research Center

Artificial Intelligence

Link Twitter Linkedin
Luis Martí

Co-lead

Inria Chile Research Center

Machine learning

Link Twitter Linkedin
Julien Salomon

Team coordinator

ANGE project team - Inria Paris

Applied mathematics

Link
Jacques Sainte-Marie

Team coordinator

ANGE project team - Inria Paris

Senior scientist

Link
Olivier Bernard

Team coordinator

BIOCORE project team - Inria Sophia-Antipolis

Modelling, optimisation and monitoring of artificial ecosystems

Link Linkedin
Michèle Sebag

Team coordinator

TAU project team - Inria Saclay

Machine learning

Link Linkedin
Marc Schoenauer

Team coordinator

TAU project team - Inria Saclay

Machine learning

Link Linkedin
Alejandro Maass

Team coordinator

Center of Mathematical Modeling (CMM) - University of Chile

Ergodic theory and systems biology

Link Linkedin
Pablo Marquet

Team coordinator

Pontifical Catholic University of Chile (PUC)

Macroecology

Link Twitter Linkedin
André Abreu

Team coordinator

Fondation Tara Océans

International relations and economic development

Link
Colomban de Vargas

Team coordinator

GO-SEE CNRS Federation

Marine biologist

Link
Damien Eveillard

Team coordinator

ComBi - Nantes University

System biologiy and oceanography

Link

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Researchers

Walid Djema

Researcher

BIOCORE project team - Inria Sophia-Antipolis

Control theory applied to biological and medical systems

Link Linkedin
Hugo Carrillo

Researcher

Inria Chile Research Center

Inverse problems, numerical analysis, mathematical modeling and simulation

Linkedin
Hernan Lira

Researcher

Inria Chile Research Center

Machine learning

Linkedin
Romain Ranini

PhD student

BIOCORE project team - Inria Sophia-Antipolis

Shiyang Yan

Postdoc

BIOCORE project team - Inria Sophia-Antipolis

Luis Valenzuela

Researcher

Inria Chile Research Center

Bioinformatics, genomics and machine learning

Andrew Berry

Researcher

Inria Chile Research Center

Machine Learning, Computer Vision


Engineering team

Andrés Vignaga

Engineering team coordinator

Inria Chile Research Center

Software engineering and architecture


Former Members

  • Dante Travisany
  • Taco de Wolff
  • Nicolás Aguilera
  • Patricio Merino
  • Victor Manuel Hidalgo Ibarra
  • Marcela Paz Osorio Thomas
  • Juan Andrés Soto Hernández

Publications and events

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Workshops and events

  • AIMOCC 2022 IJCAI Workshop – AI: Modeling Oceans and Climate Change Workshop at IJCAI 2022. Sánchez-Pi, N. and Martí, L. (eds). July 23-29, 2022. more information
  • OcéanIA IJCAI 2022 Challenge: AI methods for determining ocean ecosystems from space: Combining genomic information, microscopic and satellite imagery. Sánchez-Pi, N. and Martí, L. (eds). July 23-29, 2022. more information

Past events

  • AIMOCC 2021 – AI: Modeling Oceans and Climate Change Workshop at ICLR 2021. Sánchez-Pi, N. and Martí, L. (eds). May 7, 2021. online proceedings

Software

Click here to see the software being developed within the project.

Publications

Keynotes and talks

  1. Sanchez-Pi, N., & Martí, L. (2021). Towards a Green AI: Evolutionary Solutions for an Ecologically Viable Artificial Intelligence. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1135–1140). New York, NY, USA: Association for Computing Machinery. doi: 10.1145/3449726.3461428 slides bibtex
  2. Sanchez-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

Books

  1. Sanchez-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. (2021). OcéanIA: AI, Data, and Models for Understanding the Ocean and Climate Change. (N. Sanchez-Pi & L. Martí, Eds.). Lille/Paris/Saclay/Santiago/Sophia-Antipolis: Inria – Institut national de recherche en sciences et technologies du numérique. hal: hal-03274323 pdf bibtex

Conference papers

  1. Lira, H., Martí, L., & Sanchez-Pi, N. (2021). Frost forecasting model using graph neural networks with spatio-temporal attention. In N. Sanchez-Pi & L. Martí (Eds.), AI: Modeling Oceans and Climate Change Workshop at ICLR 2021. Santiago, Chile. hal: hal-03259658 pdf bibtex
  2. de Wolff, T., Carrillo, H., Martí, L., & Sanchez-Pi, N. (2021). Assessing Physics Informed Neural Networks in Ocean Modelling and Climate Change Applications. In N. Sanchez-Pi & L. Martí (Eds.), AI: Modeling Oceans and Climate Change Workshop at ICLR 2021. Santiago (Virtual), Chile. hal: hal-03262684 pdf bibtex
  3. Sanchez-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. hal: hal-03138712 pdf slides abstract bibtex

Online preprints

  1. de Wolff, T., Carrillo, H., Martí, L., & Sanchez-Pi, N. (2021, May). Towards Optimally Weighted Physics-Informed Neural Networks in Ocean Modelling. hal: hal-03260357 pdf bibtex

proceedings

  1. Sanchez-Pi, N., & Martí, L. (Eds.). (2021). AI: Modeling Oceans and Climate Change Workshop (AIMOCC 2021). Santiago de Chile (Virtual): Tenth International Conference on Learning Representations (ICLR 2021). bibtex

AIMOCC at ICLR 2021

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.
    • 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.

Submissions

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

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.

Organizers

  • 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.

Access

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.

AIMOCC at IJCAI 2022

AI: Modeling Oceans and Climate Change 2022

An IJCAI-ECAI 2022 Workshop

It is our distinct pleasure to invite you to the Workshop AI: Modeling Oceans and Climate Change (AIMOCC 2022) to be held in conjunction with the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022) on July 23-29, 2022, in Messe Wien, Vienna, Austria.

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.

This workshop has a related IJCAI-ECAI 2022 Challenge: AI methods for determining ocean ecosystems from space: Combining genomic information, microscopic and satellite imagery.

Topics

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.
  2. 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.

Detailed programme (in CET timezone)

  • When: 23 July 2022, 14:00-17:00 CET (08:00-11:00 CLT/EST, 09:00-12:00 BRT)
    • BRT timezone -5 hours; EST/CLT timezone -6 hours.
  • Attending in person: Room Schubert 1. Messe Wien, Vienna.
  • Attending online: Use the URI https://meet.jit.si/aimocc-2022.
  • 14:00 - 14:15. Opening comments and welcome by the organizers.

  • 14:15 - 14:40. A Physics-Informed Neural Network to Model Port Channels. Marlon S. Mathias1, Caio Fabricio Deberaldini Netto1, Marcel M.B. Barros1, Jefferson F. Coelho2, Lucas P. de Freitas1, Felipe M. Moreno1, Fabio Cozman1, Anna Helena Reali Costa 1, Eduardo Aoun Tannuri1, Edson S. Gomi 1, and Marcelo Dottori3. (1) University of São Paulo, (2) São Paulo University (POLI-USP), (3) Oceanographic Institute, University of São Paulo. abstract paper (pdf) online presentation

  • 14:40 - 15:05. Towards Optimally Weighted Physics-Informed Neural Networks in Ocean Modelling. Hugo Carrillo Lincopi, Taco de Wolff, Luis Martí, and Nayat Sánchez Pi. Inria Chile Research Center. abstract paper (pdf) online presentation

  • 15:05 - 15:30. Modeling Oceanic Variables with Dynamic Graph Neural Networks. Caio Fabricio Deberaldini Netto1, Marcel M.B. Barros1, Jefferson F. Coelho2, Felipe M. Moreno1, Marlon S. Mathias1, Lucas P. de Freitas1, Fabio Cozman1, Marcelo Dottori3, Eduardo Aoun Tannuri1, Edson S. Gomi1, and Anna Helena Reali Costa1. (1) University of São Paulo, (2) São Paulo University (POLI-USP), (3) Oceanographic Institute, University of São Paulo. abstract paper (pdf) online presentation

  • 15:30 - 16:00. Coffee break (we stay in the online call and chat).

  • 16:00 - 16:25. Enhancing Oceanic Variables Forecast in the Santos Channel by Estimating Model Error with Random Forests. Felipe M. Moreno1, Caio Fabricio Deberaldini Netto1, Marcel M.B. Barros1, Jefferson F. Coelho2, Lucas P de Freitas1, Marlon S. Mathias1, Luiz Schiaveto Neto3, Marcelo Dottori4, Fabio Cozman1, Anna Helena Reali Costa1, Edson S. Gomi1, and Eduardo Aoun Tannuri 1. (1) University of São Paulo, (2) São Paulo University (POLI-USP), (3) Escola Politécnica – University of Sao Paulo, (4) Oceanographic Institute, University of São Paulo. abstract paper (pdf) online presentation

  • 16:25 - 16:50. The BLue Amazon Brain (BLAB): A Modular Architecture of Services about the Brazilian Maritime Territory. Paulo Pirozelli1, Ais B.R. Castro1, Ana Luiza C. de Oliveira1, André Seidel1, Flávio N. Cação1, Igor C. Silveira1, João G M Campos1, Laura C. Motheo1, Leticia F. Figueiredo1, Lucas F.A.O. Pellicer1, Marcelo A. José1, Marcos M. José1, Pedro de M. Ligabue1, Ricardo S. Grava1, Rodrigo M. Tavares1, Vinícius B. Matos1, Yan V. Sym1, Anna Helena Reali Costa1, Anarosa Alves Franco Brandão2, Denis D. Maua1 Fabio Cozman1, Sarajane M. Peres1. (1) University of São Paulo, (2) Escola Politécnica – University of Sao Paulo. abstract paper (pdf) online presentation

  • 16:50 - 17:00. Final remarks.

  • 17:00 - until available. Open topic conversations.

Submissions

We welcome submissions of full papers (8 pages, not counting references) and short summary papers (4 pages, not counting references). Papers must be written in English and in PDF format according to the IJCAI-ECAI’22 style. All submitted papers will be under a single-blinded peer review for their novelty, technical quality and impact. The submissions can contain author details.

Important dates

  • Submission deadline extended! : June 4, 2022 (UTC-12) May 20, 2022 (UTC-12).
  • Notification of acceptance: June 11, 2022.
  • Reception of final version: June 18, 2022.

Post-proceedings publication

We will seek to publish selected, revised, extended papers later in a planned post-proceedings volume, to be published in the Lecture Notes in Artificial Intelligence (LNAI) series. The selection of papers will be managed by a subset of the workshop organizing committee.

Organizers

  • Nayat Sánchez-Pi, Inria Chile Research Center.
  • Pablo Marquet, Pontificia Universidad Católica de Chile.
  • Alejandro Maass, Center of Mathematical Modeling (CMM), Universidad de Chile.
  • Luis Martí, Inria Chile Research Center.

Scientific committee

  • José Manuel Molina, Universidad Carlos III de Madrid,
  • Julien Salomon, ANGE, Inria Paris,
  • Jacques Sainte-Marie, ANGE, Inria Paris,
  • Olivier Bernard, BIOCORE, Inria Sophia-Antipolis,
  • Michèle Sebag, TAU, Inria Saclay,
  • Marc Schoenauer, TAU, Inria Saclay,
  • Pablo Marquet, Pontificia Universidad Católica de Chile (PUC),
  • André Abreu, Fondation TARA Océan,
  • Ana Cristina Garcia Bicharra, Unirio - Federal University of Rio de Janeiro State,
  • Hernán Lira, Inria Chile Research Center,
  • Hugo Carrillo Lincopi, Inria Chile Research Center,
  • Andrew Berry, Inria Chile Research Center,
  • Luis Valenzuela, 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.

Access

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.

OcéanIA IJCAI 2022 Challenge

AI methods for determining ocean ecosystems from space: Combining genomic information, microscopic and satellite imagery

An IJCAI-ECAI 2022 Challenge

It is our distinct pleasure to invite you to the Challenge AI methods for determining ocean ecosystems from space: Combining genomic information, microscopic and satellite imagery to be held in conjunction with the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI-2022) on July 23-29, 2022, in Messe Wien, Vienna, Austria.

Context

The ocean is the Earth’s principal climate regulator and the main responsible for sequestering carbon dioxide (CO2). This makes it our main defense against climate change, but climate change itself is destroying the healing capacity of the ocean. Algae and, in particular, plankton, play a fundamental role in this, as they are able to remove CO2. Therefore, the mitigating capacity of an ecosystem can be established based on the presence of particular types of plankton. However, to health of the larger areas of the ocean can only be determined through large-scale measurements such as satellite imagery.

The challenge focuses on the remote identification via satellite imagery of high-potential ecosystems. This would allow large tracts of the ocean to be analyzed in a way that allows scientists and decision makers to understand how the ocean evolves over time and could be used to create policies for protecting high-value parts of the ocean. Alternatively, we propose to study the use of marker species, such as whales, which can be identified and their presence implies the existence of others.

This is an opportunity to attract the AI/ML community to this type of scientifically challenging and high-impact problem. For this we will make available to participants curated georeferenced datasets of plankton images, genomic data and satellite images and provide mentorship during the period of the challenge. It falls under the activities of Inria Project OcéanIA.

Goals

We propose to determine the variation of plankton species —i.e. ecosystems— inhabiting a given area of the ocean by cross-referencing genomic data, plankton microscope imaging and satellite images. This calls for the combined application of methods like:

  • causal inference,
  • explainable AI,
  • computer vision neural networks: representation learning, self-supervision, out of distribution detection,
  • ML methods for “small data contexts” like zero-shot/few-shot learning, and active learning, among others,
  • associative rule learning, and
  • domain adaptation and transfer learning, to mention a few.

Participation guide

The challenge will take place from 20 April 20 2022 to 29 July 2022. Teams can join the challenge at any time, but we suggest you that you do it as early as possible.

The challenge is organized in two phases:

  • Phase I: where participants work on a solution proposal and plan.
    • At the end of this phase, participants must submit a short paper (max. 2 pages excl. references) and (optionally) supplementary code.
    • Submitted proposals are evaluated, and selected ones are invited to take part of the phase II.
  • Phase II: where participants work on their challenge solutions.
    • At the end of this phase, participants should submit a full paper (max. 6 pages excl. references) and make available the supplementary code under an OSI approved license (i.e. MIT, Apache, etc.).

Datasets available to challenge participants

Getting involved

  • Join the mailing list: If you are interested to take part of the challenge, please let us know by filling up this form.
  • Join out discord server to get support, collaborate and exchange with other participants.
  • Follow Inria Chile on Twitter to for more news and updates.

Paper preparation instructions

Papers must be written in English and in PDF format according to the IJCAI-ECAI’22 style. All submitted papers will be under a single-blinded peer review for their novelty, technical quality and impact. The submissions can contain author details. See below for submission link.

Source code instructions

The challenge will help bring recent state-of-the-art AI/ML methods to tackle complex and high-impact problems that have a potential for global impact. Experts on this field have limited access and operational knowledge on how to use these advanced methods. Consequently, we will pay extra attention and involve participants in order to make their code contributions available in a form as usable as possible by non-AI/ML experts.

  • During the unfolding of the challenge source code availability (open source or private) will be left to the decision of the participants.
  • Derived and/or intermediate datasets that we consider of value will also be made freely available.
  • Upon acceptance, participants code should be made available online under an open-source friendly license, in particular it should be an OSI approved license.
  • We encourage participants to make their source code as easy to use as possible by providing installation scripts, instructions, etc.

Timeline of participation

  • Start of challenge (20 April 2022).
  • Phase I. Solution proposal preparation (20 April – 7 June 2022).
    • During this phase participants work on the conception of their solutions.
    • Participants are encouraged to interact via email or discord with organizers and other participants.
  • Submission of proposals (7 June 2022, UTC-12). Proposal submissions must include:
    • Short paper (max 2 pages excluding references) with the proposed solution, potential impact, planning, etc., and
    • Code repo (optional) link to code repository (i.e. GitHub, GitLab, etc.) with supplementary code. This code does not need to be public, but in that case organizers should have access granted to it.
    • CMT submission site: https://cmt3.research.microsoft.com/IJCAIOceanIAIChallenge2022
  • Notification of proposal acceptance (14 June 2022).
    • Challenge organizers will communicate which proposals will are accepted into Phase II.
  • Phase II. Construction of final solution (15 June – 15 July 2022).
    • During this phase accepted participants will work towards the solution to be presented in the challenge session at IJCAI-ECAI’22.
  • Submission of final solutions (16 July 2022, UTC-12): Final solution submissions must include:
  • Challenge session and awards at IJCAI-ECAI’22 (July 22-29, 2022): Participants will present their solutions in an in-person session in the conference. Only participants registered at ICJAI-ECAI’22 will be able to take part of the session.

Awards

We will provide small ocean-related gifts and cloud compute to the best contributions. Stay tuned for more details.

Publications and Post-proceedings

Dissemination is very important for the goals of the challenge. We will publish a non-archival proceedings booklet with the contributions and the main experiences gained during the challenge. Therefore, both the peer-review post volume and the challenge paper describing the results, experiences and lessons learned are interesting for us.

Organizers

Scientific committee

  • José Manuel Molina, Universidad Carlos III de Madrid,
  • Pablo Marquet, Pontificia Universidad Católica de Chile.
  • Julien Salomon, ANGE, Inria Paris,
  • Jacques Sainte-Marie, ANGE, Inria Paris,
  • Olivier Bernard, BIOCORE, Inria Sophia-Antipolis,
  • Michèle Sebag, TAU, Inria Saclay,
  • Marc Schoenauer, TAU, 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 Bicharra, 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.

Sponsorship

We are actively seeking support for different organizations. If you are interested to sponsor this challenge do not hesitate to contact us.

Diversity commitment

We will actively seek diversity in all aspects: schools of thought, theoretical backgrounds, nationalities, stages in the academic career, gender, etc. We will take an affirmative action to ensure that by disseminating the call for papers in diverse communities and offer a mentorship and assistance to help underrepresented and cross-disciplinary participants.

Software

Extract biologic subsequences
of interest from large FASTA files

Serverless cloud service. Focus on your query, not on managing storage or compute infraestructure.

Preloaded data catalog. No need to move large files around.

Access the service right from your Python code. Get query results as a Pandas DataFrame.
Run a sample query on a
Jupyter notebook now!

How does it work?

OcéanIA Platform lets you query large FASTA files in our supported data catalogs for extracting parts of biologic sequences. Just import our Python library in your code and access the query service.

ACCESS SUPPORTED DATA CATALOGS

We currently support the Ocean Microbial Reference Gene Catalog v2 (OM-RGC.v2) gene catalog from Tara Oceans Expedition. If you would like to try the service on files from other catalogs please contact us.

FOCUS ON THE QUERIES

Run your queries locally on your workstation either through a Python script or a Jupyter notebook. Install our Python package, then import our Python module in your code, and you are ready to go.

QUERY FASTA FILES

Extract multiple specific gene sequences from a file in a single query.

  

How to use

  • Run pip install oceania-query-fasta to install the Python package that enables access to query service.
  • From your Python code (either a script or a notebook) import the oceania module.
  • Select a file from the catalog, define your query, apply the query to the file. The result can be either retrieved as a Pandas DataFrame or saved as a CSV file.
A Jupyter notebook with a minimal example of a query that extracts a set of sequences from a given file in the catalog.


A Jupyter notebook with an example that extracts large intergenic regions (IGRs) from one surface sample.

news

Diatoms_through_the_microscope.jpg

Others

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10 Mar 2021

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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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How it works

Oceania-query-fasta is a Python package that is built to make queries in Ocean Microbial Reference Gene Catalog v2 ~100GB (gziped) of FASTA, CSV and TSV files.

Install library oceania-query-fasta

Open complex Demo in Google Colab

See complex Demo in Jupyter NbViewer

View Github Demo code

Download simple Jupyter Notebook

Download complex Jupyter Notebook

Feedback report 
pip install oceania-query-fasta
STORAGE_KEY = "data/raw/tara/OM-RGC_v2/assemblies/TARA_A100000171.scaftig.gz"
POSITIONS = [
    ["TARA_A100000171_G_scaffold48_1", 10, 50, "complement"],
    ["TARA_A100000171_G_scaffold48_1", 10, 50],
    ["TARA_A100000171_G_scaffold48_1", 10, 50, "reverse_complement"],
    ["TARA_A100000171_G_scaffold181_1", 0, 50],
    ["TARA_A100000171_G_scaffold181_1", 100, 200],
    ["TARA_A100000171_G_scaffold181_1", 200, 230],
    ["TARA_A100000171_G_scaffold493_2", 54, 76],
    ["TARA_A100000171_G_scaffold50396_2", 87, 105],
    ["TARA_A100000171_G_C2001995_1", 20, 635],
    ["TARA_A100000171_G_C2026460_1", 0, 100],
  ]
results = get_sequences_from_fasta(
    STORAGE_KEY,
    POSITIONS
)
print(results)

Features:

  • Usage from Jupyter, command-line and Python package
  • Queries of Ocean Microbial Reference Gene Catalog (OM-RGC_v2)
  • Support custom queries
  • Output format CSV or FASTA
  • Free Cloud Computing (Serverless)