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Machine Learning for Sustainability
Thomas Dietterich · J. Zico Kolter · Matthew A Brown

Fri Dec 10:30 PM -- 11:00 AM PST @ Melia Sierra Nevada: Guejar
Event URL: http://people.csail.mit.edu/kolter/mlsust11/ »

Sustainability problems pose one of the greatest challenges facing society. Humans consume more than 16TW of power, about 84% of which comes from unsustainable fossil fuels. In addition to simply being a finite resource, the carbon released from fossil fuels is a significant driver of climate change and could have a profound impact on our environment. In addition to carbon releases, humans are modifying the ecosphere in many ways that are leading to large changes in the function and structure of ecosystems. These include huge releases of nitrogen from fertilizers, the collapse and extinction of many species, and the unsustainable harvest of natural resources (e.g., fish, timber). While sustainability problems span many disciplines, several tasks in this space are fundamentally prediction, modeling, and control tasks, areas where machine learning can have a large impact. Many of these problems also require the development of novel machine learning methods, particularly methods that can scale to very large spatio-temporal problem instances.

In recent years there has been growing interest in applying machine to problems of sustainability, spanning applications in energy, environmental management, and climate modeling. The goal of this workshop will be to bring together researchers from both the machine learning and sustainability application fields to continue and build upon this emerging area. The talks and posters will span general discussions of sustainability issues, specific sustainability-related data sets and problem domains, and ongoing work on developing and applying machine learning techniques to these tasks.

Author Information

Tom Dietterich (Oregon State University)

Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor and Director of Intelligent Systems Research at Oregon State University. Among his contributions to machine learning research are (a) the formalization of the multiple-instance problem, (b) the development of the error-correcting output coding method for multi-class prediction, (c) methods for ensemble learning, (d) the development of the MAXQ framework for hierarchical reinforcement learning, and (e) the application of gradient tree boosting to problems of structured prediction and latent variable models. Dietterich has pursued application-driven fundamental research in many areas including drug discovery, computer vision, computational sustainability, and intelligent user interfaces. Dietterich has served the machine learning community in a variety of roles including Executive Editor of the Machine Learning journal, co-founder of the Journal of Machine Learning Research, editor of the MIT Press Book Series on Adaptive Computation and Machine Learning, and editor of the Morgan-Claypool Synthesis series on Artificial Intelligence and Machine Learning. He was Program Co-Chair of AAAI-1990, Program Chair of NIPS-2000, and General Chair of NIPS-2001. He was first President of the International Machine Learning Society (the parent organization of ICML) and served a term on the NIPS Board of Trustees and the Council of AAAI.

J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Zico Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, and also serves as Chief Scientist of AI Research for the Bosch Center for Artificial Intelligence. His work focuses on the intersection of machine learning and optimization, with a large focus on developing more robust, explainable, and rigorous methods in deep learning. In addition, he has worked on a number of application areas, highlighted by work on sustainability and smart energy systems. He is the recipient of the DARPA Young Faculty Award, and best paper awards at KDD, IJCAI, and PESGM.

Matthew A Brown (University of Bath)

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