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Mini Symposium
Machine Learning for Sustainability
J. Zico Kolter · Thomas Dietterich · Andrew Y Ng

Thu Dec 10 01:30 PM -- 04:30 PM (PST) @ Regency D
Event URL: http://www.cs.stanford.edu/group/nips09-mlsust/ »

The world has a sustainability problem. Humans currently consume an average of 16TW of power (and rising), more than 86% of which comes from (unsustainable) fossil fuels. There is a range of estimates as to when this supply will run out, but this is a scenario that may well happen within our lifetimes. Even more pressing is the effect that such fuels have on our climate: given no attempts to reduce the world's fossil fuel usage, even the most conservative climate models predict that the world temperature will increase by over five degrees (Fahrenheit) in the next 90 years, an increase that could cause ecological disasters on a global scale. Building a sustainable infrastructure for energy and ecosystems is shaping up to be one of the grand scientific and political challenges of the 21st century. Furthermore, there is a growing consensus that many aspects of sustainability are fundamentally information systems problems, tasks where machine learning can play a significant role.

This mini-symposium will bring together leading researchers with both machine learning backgrounds and energy/sustainability backgrounds to address the question: how can machine learning help address the world's sustainability problem? The mini-symposium will also seek to answer: What is the current state of work directed at sustainability, energy, and ecology in the machine learning, operations research, and optimization communities? What are the primary scientific and technical challenges in information processing for sustainability? And finally, what are (and what aren't) areas where machine learning can make a genuine impact on the science of sustainability?

Because this is an emerging field of research, the talks at this symposium will aimed at the general NIPS audience. There is a growing number of researchers working in sustainability, but even more broadly, we think that such problems have the potential to advance basic machine learning in a manner similar to other important applications, such as computer vision, natural language processing, and computational biology. Sustainability problems offer an equally rich set of domains, and solutions to these problems will have a genuine impact on the world.

Author Information

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.

Thomas 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.

Andrew Y Ng (DeepLearning.AI)

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