2026
- SIAM Math for Planet Earth 2026 (Plenary)
- Earth System Science Interdisciplinary Center (ESSIC), University of Maryland
- UC Davis Atmospheric Sciences: “Beyond the bonanza: AI as a science discovery engine”
- German Aerospace Center (DLR): “Beyond the bonanza: AI as a science discovery engine”
- Sorbonne University: “Beyond the bonanza: AI as a science discovery engine”
- National Institute for Research in Digital Science and Technology (INRIA): “Beyond the bonanza: AI as a science discovery engine”
- Technische Universität München (TUM): “AI as a science discovery engine”
- UC Davis Civil and Environmental Engineering: “Why you should be wary of unicorns: Beyond the AI Bonanza”
- Potsdam Institute for Climate Impact Research: “Why you should be wary of unicorns: Beyond the AI Bonanza”
2025
- American Meteorological Society Annual Meeting: “Trustworthy AI for Ocean Dynamics: A Semantics-Driven Approach to Southern Ocean Circulation”
- LLNL–UC Davis joint symposium: “Bridging AI, Ocean and Climate Science: Advancing Prediction and Decision Support”
- UC Davis Applied Mathematics Graduate Group day: “Trustworthy AI for Ocean Dynamics: A Semantics-Driven Approach to Southern Ocean Circulation”
- Max Planck Institute Jena ELLIS Summer School: “Beyond the AI Bonanza: Interpretable Machine Learning for Physical Oceanography”
- European Centre for Medium-Range Weather Forecasts (ECMWF): “A piece of science for every stage”
- National Oceanography Centre Southampton: “Objective taxonomies in ocean physics for insight and prediction”
2024
- 6th NOAA AI Workshop: “AI for new knowledge and education”
- UC San Diego, Scripps Institution of Oceanography: “Deciphering Southern Ocean Circulation: New Perspectives Through Machine Learning”
- Dynamics Days: “Equations as emergent phenomena determined using machine learning: An ocean case study”
2023
- UC Davis Atmospheric Science: “Physics-Informed Machine Learning to Push the Ocean Frontier in Climate”
- UC Davis Applied Mathematics: “Physics-Informed Machine Learning to Push the Ocean Frontier in Climate”
- CLIVAR Predictability, Predictions, and Applications Interface: “Escaping 'black box' machine learning to increase trust and accelerate discovery”
- United Nations International Telecommunication Union: “Physics-informed ML to push the ocean frontier in climate”
- University of Toronto Nobel Seminar Series: “Elucidating driving mechanisms in the North Atlantic and Southern Ocean dynamics: Physics-informed and trustworthy ML for ocean science”
- University of Liège, 54th international Liège colloquium on ocean dynamics
- UCLouvain: “Physics-informed machine learning to push the ocean frontier in climate: A Southern Ocean case study”
- Institut Pierre-Simon Laplace (IPSL): “Physics-informed machine learning to push the ocean frontier in climate: A Southern Ocean case study”
- University of Miami: “Elucidating Driving Mechanisms in North Atlantic and Southern Ocean Dynamics: Physics-Informed and Trustworthy Machine Learning for Ocean Science”
- UC Davis Computer Science: “Physics-informed and trustworthy computation for climate resilience”
- Sorbonne University: “Elucidating Driving Mechanisms in North Atlantic and Southern Ocean Dynamics: Physics-Informed and Trustworthy Machine Learning for Ocean Science”
2022
- CLIVAR Physical Oceanography: “A supergyre in the Southern Ocean modulates the global overturning: insight guided by interpretable machine learning”
- Climate Informatics: “Asking how the Southern Ocean responds to global heating and understanding why the answer emerged”
- University of Cambridge: “Intelligent solutions to monitor ocean health”
- SIAM Mathematics of Data Science: “Developing and Learning from Trust in Machine Learning”
- SIAM Geosciences Webinar Series: “Understanding the Ocean’s response in a Future Climate”
- Max Planck Institute for Meteorology: “The response of the ocean’s overturning to global warming: A robust blueprint for trustworthy AI for climate analysis”
- Institute for Mathematical and Statistical Innovation (IMSI), University of Chicago: “Elucidating drivers of Southern Ocean circulation change: A blueprint for interpretable and explainable machine learning”
- MIT Earth, Atmospheric and Planetary Sciences: “Intelligent solutions to monitor and predict ocean health”
- MIT Mechanical Engineering: “Intelligent solutions to monitor and predict ocean health”
- UC Berkeley: “Data driven understanding of ocean systems”
- University of Liège: “Intelligent solutions to monitor and predict ocean health”
- University of Wisconsin–Madison: “Revealing the impact of climate change on North Atlantic circulation using transparent machine learning”
- University of Rhode Island: “Revealing the impact of climate change on North Atlantic circulation using transparent machine learning”
2021
- American Geophysical Union (AGU): “Revealing the impact of climate change on North Atlantic circulation using transparent machine learning”
- Department of Energy AI workshop: “Ocean Grand Challenges”
- Climate Change AI webinar: “A robust blueprint for trustworthy AI for climate analysis”
- NOAA AI workshop: “Revealing the impact of climate change on North Atlantic circulation using transparent machine learning”
- US National Center for Atmospheric Research CGD Series: “Revealing mechanisms of change in the Atlantic Meridional Overturning Circulation under global heating”
- KITP ML for Climate Physics: “Revealing the Impact of Global Heating on the Meridional Overturning Circulation with transparent machine learning”
- IMSI, University of Chicago: “Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces”
- GEOMAR Helmholtz Centre for Ocean Research: “Solutions to understand and monitor the ocean and climate”
- Summit for Incorporating Data Science and Open Science in Aquatic Research: “Understanding the ocean’s response in a future climate: A robust blueprint for trustworthy AI for climate analysis”
- International Conference on Machine Learning: “Revealing the impact of global warming on climate modes using transparent machine learning”
- UC Santa Cruz: “Revealing the impact of global warming on ocean circulation: A robust blueprint for trustworthy AI for climate analysis”
- Scripps Institution of Oceanography: “Revealing the Impact of Global Heating on the Meridional Overturning Circulation”
- Potsdam Institute for Climate Impact Research: “Revealing the Impact of Global Heating on the Meridional Overturning Circulation”
- Technical University of Munich: “Revealing the Impact of Global Heating on the Meridional Overturning Circulation”
- University of Washington: “Revealing the Impact of Global Heating on the Meridional Overturning Circulation”
2020
- Second NOAA Workshop on Leveraging AI in the Environmental Sciences: “Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces”
- Los Alamos National Laboratory: “Living on the Manifold: A geography of ocean dynamical regimes from eddy to global scale”
- University of Washington, Department of Mechanical Engineering: “Living on the Manifold: A geography of ocean dynamical regimes from eddy to global scale”
- University of Washington, Department of Ocean Sciences: “Living on the Manifold: A geography of ocean dynamical regimes from eddy to global scale”
- University of Washington: “Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces”
- University of British Columbia: “Ocean exploration with machine learning: An Antidote to Chaos?”
2019
- AGU: “Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions”
- University of Bergen: “Ocean exploration with machine learning: An Antidote to Chaos?”
- Princeton University: “Ocean exploration with machine learning: An Antidote to Chaos?”
- Norway–US bilateral AI workshop: “Elucidating ocean ecological complexity”
- Norway–US bilateral AI workshop: “Recognising ocean physical regimes”
- Woods Hole Oceanographic Institution: “Ocean exploration with machine learning: An Antidote to Chaos?”
- University of Tromsø: “Ocean exploration with machine learning: An Antidote to Chaos?”
2018
- MIT: “Machine learning for global biogeography?”
- Columbia University, LDEO: “Linear predictability: A sea surface height case study”
2017
- Stony Brook University: “Linear predictability: A sea surface height case study”
Others 2012–2016: Yale University, WHOI, MIT, University of Texas at Austin, University of Washington, Oregon State University, University of Oxford, University of Bristol, National Oceanography Centre Southampton (NOCS), MONCACO meeting.