As a Human-Computer Interaction researcher, I design and build social computing systems (e.g., social media, workplace software) grounded in affirmative consent, an idea that a person or a system must ask for, and earn, enthusiastic approval before interacting with an individual. Consent is an important concept to define for software and policy design to tackle socio-technical problems—these span companies’ massive data collection and inferences about users, especially with the rise of AI, to harmful interpersonal dynamics mediated by technology that disproportionately impact marginalized groups.
My research advanced the theory of affirmative consent by applying its properties (affirmative consent is voluntary, informed, revertible, specific, and unburdensome) to the design of social computing systems (CHI 2021). Based on such theory, I create systems and interfaces, and evaluate them by conducting field studies and experiments. Concretely, my directions include:
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 principles of affirmative consent, has attracted around 50 users so far and 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 advertisement privacy controls, which was previously covered by The Wall Street Journal.
I am on the academic job market for 2024-2025.