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AddressRoom 107, 1st floor, Tour 44, Campus Pierre et Marie Curie, 4 Place Jussieu
Abstract
The stunning recent advances in AI chatbots rely on cutting-edge generative deep learning algorithms and architectures trained on massive amounts of text data. Generative deep learning has also shown remarkable results when trained on video data and on combinations of different data types (i.e., multi-modal). The recent advances in generative deep learning can also benefit a variety of applications for addressing climate change. For example, generative deep learning trained on climate and weather data can be a powerful tool in generating an ensemble of weather predictions and in quantifying the uncertainty of long-term projections of climate change.
As opposed to text and video, the relevant training data for this domain includes weather and climate data from observations, reanalyses, and even physical simulations. As in many massive data applications, creating "labeled data" for supervised machine learning is often costly, time-consuming, or even impossible. Fortuitously, in very large-scale data domains, "self-supervised" machine learning methods are now actually outperforming supervised learning methods. In this lecture, I will survey our lab's work developing generative and self-supervised machine learning approaches for applications addressing climate change, including detection and prediction of extreme weather events, and downscaling and temporal interpolation of spatiotemporal data. Our methods address problems such as forecasting the path and intensity of tropical cyclones, renewable energy planning, and projecting future sea-level rise.
Speaker
Claire Monteleoni is a Choose France Chair in AI and a Research Director at INRIA Paris, a Professor in the Department of Computer Science at the University of Colorado Boulder (on leave), and the founding Editor in Chief of Environmental Data Science, a Cambridge University Press journal launched in December 2020. Her research on machine learning for the study of climate change helped launch the interdisciplinary field of Climate Informatics. She co-founded the International Conference on Climate Informatics, which will hold its 14th annual event in 2025. She gave an invited tutorial: Climate Change: Challenges for Machine Learning, at NeurIPS 2014. She currently serves on the U.S. National Science Foundation's Advisory Committee for Environmental Research and Education, and as Tutorials co-Chair for the International Conference on Machine Learning (ICML) 2024 and 2025.
Practical information
Date : 29th of January 2025, 5 pm
Location : Room 107, 1st floor, Tour 44, Campus Pierre et Marie Curie, 4 Place Jussieu