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Claire Monteleoni - Algorithms for Climate Informatics: Learning from spatiotemporal data with both spatial and temporal non-stationarity

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Climate Informatics is emerging as a compelling application of machine learning. This is due in part to the urgent nature of climate change, and its many remaining uncertainties (e.g. how will a changing climate affect severe storms and other extreme weather events?). Meanwhile, progress in climate informatics is made possible in part by the public availability of vast amounts of data, both simulated by large-scale physics-based models, and observed. Not only are time series at the crux of the study of climate science, but also, by definition, climate change implies non-stationarity. In addition, much of the relevant data is spatiotemporal, and also varies over location. In this talk, I will discuss our work on learning in the presence of spatial and temporal non-stationarity, and exploiting local dependencies in time and space. Along the way, I will highlight open problems in which machine learning, including deep learning methods, may prove fruitful.

Bio: Claire Monteleoni is a Jean d’Alembert Fellow at the University of Paris-Saclay, hosted by CNRS, and an Associate Professor of Computer Science at George Washington University. Previously, she was research faculty at the Center for Computational Learning Systems, at Columbia University. She did a postdoc in Computer Science and Engineering at the University of California, San Diego, and completed her PhD and Master’s in Computer Science at MIT. She holds a Bachelor’s in Earth and Planetary Sciences from Harvard. Her research focuses on machine learning algorithms and theory for learning from data streams, spatiotemporal data, raw (unlabeled) data, and private data, and applications with societal benefit. Her research on machine learning for the study of climate science received the Best Application Paper Award at NASA CIDU 2010, and helped launch the interdisciplinary field of Climate Informatics. In 2011, she co-founded the International Workshop on Climate Informatics, which turned 7 in 2017, and has attracted climate scientists and data scientists from over 19 countries and 30 U.S. states. She gave an invited tutorial on climate informatics at NIPS 2014. She recently served as a Senior Area Chair for NIPS 2017, and she is an Area Chair for ICML 2018.Climate Informatics is emerging as a compelling application of machine learning. This is due in part to the urgent nature of climate change, and its many remaining uncertainties (e.g. how will a changing climate affect severe storms and other extreme weather events?). Meanwhile, progress in climate informatics is made possible in part by the public availability of vast amounts of data, both simulated by large-scale physics-based models, and observed. Not only are time series at the crux of the study of climate science, but also, by definition, climate change implies non-stationarity. In addition, much of the relevant data is spatiotemporal, and also varies over location. In this talk, I will discuss our work on learning in the presence of spatial and temporal non-stationarity, and exploiting local dependencies in time and space. Along the way, I will highlight open problems in which machine learning, including deep learning methods, may prove fruitful.

 

Dates: 
Tuesday, April 17, 2018 - 11:00 to 12:00
Location: 
Inria Lille - Nord Europe, bâtiment A, salle plénière
Speaker(s): 
Claire Monteleoni
Affiliation(s): 
George Washington University and Jean d'Alembert Fellow, Université Paris-Saclay