Current research projects
New vulnerabilities in cyber-physical infrastructures
Increasingly, critical physical infrastructures are being distributed and networked. For example, in the smart grid, electricity meters are being updated and connected to the Internet, smart thermostats are sensing and actuating loads in households, and microgrids and renewables are being deployed to decentralize generation. In traffic networks, centralized optimization algorithms are beginning to gain popularity to determine ramp metering policies, and traffic-aware map services such as Google Maps are directing the flow of a significant portion of traffic usage. In healthcare systems, more and more patient data is being stored in databases, which allow for the use of big data analytics to find underlying causes and unifying trends in illnesses, as well as improve doctor-to-doctor communications.
However, as demonstrated in many recent news articles as well as depicted in many dramatized movies and video games, this presents new opportunities for eavesdropping, denial of service attacks, and spoofing, and users are at risk from both a security and a privacy viewpoint.
tl;dr: find new security and privacy risks in cyber-physical systems (cps), motivate behaviors for societal benefit, and quantify the utility of data and its tradeoff with privacy.
Image source: Back (comic series)
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.
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.