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. A lack of meaningful consent-granting mechanisms gives rise to a range of problems around power, safety, and privacy. For example, companies collect and share user data without users even knowing, especially with the rise of AI—oftentimes, there is no real consent. Many social technologies are also designed without considering users’ consent (e.g., platforms' impoverished privacy settings), which disproportionately impacts the safety of marginalized groups.

Addressing such problems is challenging because there has been a lack of a clear definition of consent that can be applied to systems across domains. My research has advanced the theory of affirmative consent by normatively defining its properties. To demonstrate the applicability of this theory, I design, build, and deploy consentful systems and interfaces. Specifically, I:

• theorize what affirmative consent is—affirmative consent is voluntary, informed, revertible, specific, and unburdensome; and introduce such theory to the design of social computing systems (CHI 2021)
• build consentful systems—I embed affirmative consent into multiple layers of social platforms, spanning core functionalities, interfaces, and algorithms; e.g., Moa, a consentful platform that enables sensitive information-sharing among people with less power within an organization, without knowing each other's identities; consent-granting mechanisms for algorithmic inferences (CHI 2024)
• rethink data privacy interfaces and business models—because tech companies argue they obtain users’ consent via interfaces that are heavily impacted by their business models (CHI 2023, ongoing work)

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.