Recent years have brought increasing levels of surveillance. I am interested in designing privacy-preserving algorithms that enable people to communicate freely without sacrificing privacy. I have been working on a few main problems within this theme, related to anonymous social media (e.g., Yik Yak, Secret) and anonymous peer-to-peer networks (e.g., Bitcoin, cryptocurrencies). A common theme in this work is that we wish to provide statistical anonymity guarantees against computationally-unbounded adversaries.
Graph-structured data arises frequently in modern applications, including in social graphs, sensor networks, and biological networks. The goal of this work is to develop a framework for dealing with signals that are defined over arbitrary graphs, analogous to classical signal processing defined over regular domains (e.g. spatial grid, discrete-time).
Content-based media searches (e.g., facial recognition) are central to modern surveillance techniques. However, these techniques can reveal a great deal of information to servers processing requests. As surveillance increases in depth and breadth, it is important to devise privacy-preserving alternatives.
NextScholars (Mentor): Mentorship program for young women interested in STEM (2017-present)
SEED (Mentor): Guided groups of Berkeley High School students in yearlong research projects on nuclear power, GMOs, and food deserts. (2012-2013)
TechBridge (Volunteer): Worked with groups of elementary-school girls on projects related to coding and basic electrical circuits. (2012-2014)