Current research projects
Energy disaggregation, also known as nonintrusive load monitoring (NILM), is the task of separating aggregate energy data for a whole building into the energy data for individual appliances. Studies have shown that simply providing disaggregated data to the consumer improves energy consumption behavior. Furthermore, this disaggregated data could be used to improve efficiency in energy distribution, provide data to control algorithms in advanced metering infrastructures, or even help target advertising. However, placing individual sensors on every device in a home is not presently a practical solution. I've been working on using a dynamical systems approach to disaggregation, which hopes to improve disaggregation results by modeling the power consumption dynamics of individual devices. This work is joint with Lillian Ratliff and Professor Henrik Ohlsson.
Privacy/security in the power grid
The advent of smart-grid technologies promises much more efficient electricity usage across the grid. However, such gains are made at the risk, potentially, of both the security of the grid itself as well as the privacy of the individual consumer. Currently, I'm working on ways to understand the relationship between the utility company and the consumer, focusing on a systems-theoretic perspective. This work is joint with Professor Alvaro Cárdenas, Dr. Galina Schwartz, and Professor Saurabh Amin.
Past research projects
Nonlinear basis pursuit
The field of compressive sensing has generally concerned itself with recovering data from the output of a linear system. Recent work has been done to generalize the methodologies of compressive sensing to more general classes of functions. This work was done with Professor Henrik Ohlsson and Dr. Allen Yang. More details can be found at: http://nonlinearcs.blogspot.com.
Ionic polymer-metal composites
During my undergraduate studies, my research centered on system identification of ionic polymer-metal composites. Given the complicated physical dynamics, we attempted to perform black-box modeling, and extract the physical parameters which were most salient to performance. I worked to derive a temperature-dependent model for the IPMC, and verified the model's efficacy in improving results during open-loop control. This work was done under the guidance of Professor Xiaobo Tan.