Research interests

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.


My research has been focused on a few perspectives on these new vulnerabilities:

  1. Identify the parties interested in exploitation of these new vulnerabilities, the motives of these parties, and which part of the cyber-physical infrastructure is most likely to be attacked by these parties.

  2. Design markets and incentive programs so the users of these cyber-physical infrastructures are motivated to help the stable, efficient operation of the large-scale, distributed systems.

  3. Provide quantitative analysis of the value of data to the infrastructure's operation, and the private information about users contained within the data.

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.

This work is joint with Lillian Ratliff, Professor Henrik Ohlsson, and Professor Alvaro Cárdenas.

Image source: Back (comic series)

Energy disaggregation

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:

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.