Despite the scientific consensus on climate change, drastic uncertainties remain. Crucial questions about changes in regional climate, trends of extreme events such as heat waves, heavy precipitation, and mega-storms, and understanding how climate varied in the distant past, must be answered in order to improve predictions, assess impacts and vulnerability, and aid mitigation and adaptation efforts. Machine learning can help answer such questions and shed light on climate change. Similar to the case of bioinformatics, the study of climate change provides a data rich scientific domain in which cutting-edge tools from machine learning can make a major impact. Further, such questions give rise to new challenges for the design of machine learning algorithms. I will give an overview of challenge problems in climate informatics, and present recent work from my research group in this nascent field, with a particular focus on improving predictions of climate change trends from ensembles of climate simulations, and improving the understanding of extreme events.
Claire Monteleoni is an assistant 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 Masters in Computer Science, at MIT. She holds a Bachelors in Earth and Planetary Sciences from Harvard. Her research focuses on machine learning algorithms and theory for problems including learning from data streams, learning from raw (unlabeled) data, learning from private data, and climate informatics: accelerating discovery in climate science with machine learning. Her work on climate informatics received the Best Application Paper Award at NASA CIDU 2010. In 2011, she co-founded the International Workshop on Climate Informatics, which is now in its seventh year, attracting climate scientists and data scientists from over 19 countries and 30 U.S. states.