Abstract:
The threat of climate change is one of the greatest challenges currently facing society. Given the profound impact machine learning has made on the natural sciences to which it has been applied, such as the field of bioinformatics, machine learning is poised to accelerate discovery climate science. Our recent progress on climate informatics reveals that collaborations with climate scientists also open interesting new problems for machine learning. I will give an overview of challenge problems in climate informatics, and present recent work from my research group in this nascent field.
A key problem in climate science is how to combine the predictions of the multi-model ensemble of global climate models that inform the Intergovernmental Panel on Climate Change (IPCC). Our Tracking Climate Models (TCM) work demonstrated the promise of an algorithm for online learning with expert advice, for this task. Given temperature predictions from 20 IPCC global climate models, and over 100 years of historical temperature data, TCM tracked the changing sequence of which model currently predicts best. On historical data, at both annual and monthly time-scales, and in future simulations, TCM consistently outperformed the average prediction over climate models, the existing benchmark in climate science. Recently, we have extended TCM to take into account climate model predictions at higher spatial resolutions, and to model geospatial neighborhood influence between regions. Our algorithm enables neighborhood influence by modifying the transition dynamics of the Hidden Markov Model from which TCM is derived, allowing the performance of spatial neighbors to influence the temporal switching probabilities for the best climate model at a given location.
Bio:
Claire Monteleoni is an assistant professor of Computer Science at The George Washington University, which she joined in 2011. Previously, she was research faculty at the Center for Computational Learning Systems, and adjunct faculty in the Department of Computer Science, at Columbia University. She was 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. Her research focus is 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 papers have received several awards. In 2011, she co-founded the International Workshop on Climate Informatics, which she co-chaired again this year. She serves on the Editorial Board of the Machine Learning Journal, and recently served on the Senior Program Committees of ICML and UAI, 2012.

Location:	Lewis K. Downing Hall ;Theodore R. Hagans, Jr. Reading Room -2019
Sponsor:	Systems & Computer Science
Contact:	Dr. Muguzi Rwebangira rweba@scs.howard.edu 202-806-6650

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