10/2/2017: Zinan Lin was named a CMU Presidential Fellow for the 2017-2018 academic year. Congratulations, Zinan!
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.
Physical diffusion phenomena like epidemics and viral videos can be represented as random processes on graphs. I am interested in theoretically and empirically studying the spread and dynamics of these processes. These random processes appear in a variety of application domains, including social media, cryptocurrencies, and fake news.
Generative adversarial networks (GANs) are a technique for learning a generative model from data. They have been tremendously successful at producing high-quality, sharp images. However, they are not well-understood. I am interested in studying the dynamics of GANs themselves, as well as using them for the release of privacy-preserving datasets.
NextScholars (Mentor): Mentorship program for young women interested in STEM (2017-2018)
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)