Call for Participation:
NIPS 2009 Mini Symposium on Machine Learning for Sustainability
This mini symposium, held at the Neural Information Processing Systems
conference, will feature invited talks by leading researchers working
in both machine learning and energy/sustainability.
Key Information:
* Date: Thursday, December 10th, 2009
* Time: 1:30PM - 4:30PM
* Room: Regency C, 3rd Floor
* Location: Hyatt Regency, Vancouver, CA
* Conference: NIPS 2009
* Contact: [email protected]
* Website: http://www.cs.stanford.edu/group/nips09-mlsust/
Schedule:
* 1:30-1:40 - Opening Remarks
* 1:40-2:05 - David Waltz - Machine learning for the NYC power grid:
lessons learned and the future
* 2:05-2:30 - Saul Griffith - What it takes to win the carbon war. Why
even AI is needed.
* 2:30-3:00 - Coffee Break
* 3:00-3:25 - Thomas Dietterich - Ecological Science and Policy:
Challenges for Machine Learning
* 3:25-3:50 - Carlos Guestrin - Optimizing Information Gathering in
Environmental Monitoring
* 3:50-4:15 - Warren Powell - Approximate Dynamic Programming in
Energy Resource Management
* 4:15-4:25 - Closing Remarks
About:
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.
Organizers:
J. Zico Kolter, Stanford Unversity
Thomas Dietterich, Oregon State University
Andrew Ng, Stanford University
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