As a Human-Computer Interaction researcher, I design and build social computing systems grounded in affirmative consent, the idea that a person or a system must ask for, and earn, enthusiastic approval before interacting with an individual. Consent is deeply related to tackling a range of problems around power, safety, and privacy. For example, companies collect and share user data without users even knowing—there is often no real consent. It is also difficult to pick and choose—to give consent to—who can see one’s social media posts, given platforms' impoverished privacy settings. Users from marginalized groups face additional risks, such as online harassment.
To develop solutions to address such problems, understanding what consent is critical. My research has advanced the theory of affirmative consent by normatively defining its properties—to rethink the role of consent in the field of computing, and to reimagine social platforms. Based on this theory, I design and build systems and interfaces. Specifically, I:
I am a Ph.D. candidate at the University of Michigan School of Information and the Department of Computer Science & Engineering, advised by Professor Kentaro Toyama. My research has been recognized with a Meta Research PhD Fellowship (selected on my fourth try), University of Michigan Barbour Scholarship, EECS Rising Star, and two honorable mentions. I am also committed to practical impact—Moa, a platform I built to enable sensitive information sharing based on affirmative consent principles, has provided real-world help to PhD students navigating challenging PhD advising dynamics. I have also been invited by the Federal Trade Commission to present my research on designing ad privacy controls, which was previously covered by The Wall Street Journal.
I am on the academic job market for 2024-2025.