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Zico Kolter 9/26/12, Noon, BH 129, Building an Informatics of Personal Energy Consumption

Zico Kolter, an incoming Assistant Professor in the School of Computer Science at Carnegie Mellon and currently a postdoctoral fellow in the Computer Science and Artificial Intelligence Laboratory at MIT, will be the CEIC’s featured speaker on Wednesday, September 26, 2012. He will present a seminar entitled “Building an Informatics of Personal Energy Consumption” at 12:00 noon (Lunch will be provided at 11:50am). This seminar will take place in the Engineering and Public Policy conference room located in Baker Hall 129. This seminar is part of the Carnegie Mellon Electricity Industry Center Series. To learn more please visit http://wpweb2.tepper.cmu.edu/ceic/.

Please sign up at the following Doodle website by Tuesday, September 25, 12:00 noon:

http://doodle.com/574sbtbkf6rtma94

PLEASE NOTE, by signing up for this seminar, you agree to the following: “I understand that if I indicate that I am coming, but then do not attend and sign the attendance sheet, or cancel the day before the seminar, the department will bill me for $8.00 which I agree to pay.” You also have the option to decline lunch.

Details:
Zico Kolter

“Building an Informatics of Personal Energy Consumption”
Wednesday, September 26, 2012
12:00 noon (lunch served at 11:50pm)
Engineering and Public Policy Conference Room (Baker Hall 129)

If you plan to attend, please complete the Doodle poll by Tuesday September 25:

http://doodle.com/574sbtbkf6rtma94

ABSTRACT: An important characteristic of many modern energy domains is that they can produce large amounts of data, such as detailed personal consumption information, on an unprecedented scale. The ability to understand this data, to make inferences and predictions about energy information, can play a transformative role in the future of energy systems. In this talk I will discuss how recent advances in machine learning and data analysis can be brought to bear on such problems, and now these problems can themselves motivate new statistical methods. In particular, I will highlight new algorithmic work on energy disaggregation, the task of taking an aggregate power signal and decomposing it into separate devices. This ability helps us understand how energy is consumed in a building, and studies have shown that just presenting this information to users can often lead to large energy savings. I will also discuss work on city-level energy analysis, and how this can inform both customers and cities about the relative energy consumption between homes.

BIOGRAPHICAL SKETCH: . Zico Kolter is an incoming Assistant Professor in the School of Computer Science at Carnegie Mellon and currently a postdoctoral fellow in the Computer Science and Artificial Intelligence Laboratory at MIT. He received his his Ph.D. in Computer Science from Stanford University in 2010 and his B.S. from Georgetown University in 2005. His research revolves around sustainable energy domains, with a focus on core learning, inference, and control tasks within this space. His work in this area include projects in energy disaggregation, wind turbine control, and modeling building energy consumption. His past work also looked at learning and control methods in other domains, including autonomous cars in extreme maneuvers, quadruped locomotion, and feature selection in reinforcement learning. He is the recipient of an NSF Computing Innovation Postdoctoral Fellowship, a former recipient of an NSF Graduate Research Fellowship, and has received best paper awards at the SIGKDD and AIAA Infotech@Aerospace conferences.

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