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 has advanced the theory of affirmative consent by normatively defining its properties for the design of social computing systems. Based on such theory, I create systems and interfaces, and evaluate them by conducting field studies and experiments. Concretely, my directions include:

• theorizing what affirmative consent is–affirmative consent is voluntary, informed, revertible, specific, and unburdensome, and introducing such theory to the design of social computing systems (CHI 2021)
• building systems to investigate how to embed affirmative consent into multiple layers of social platforms—spanning core functionalities, interfaces, and algorithms; e.g., Moa, a platform that gives users more precise control over their posts’ audience, without knowing other users’ identities (in submission), consent mechanisms for AI-based inferences (CHI 2024)
• rethinking data privacy interfaces and social media’s business models for user’ consent—because tech companies obtain users’ consent via interfaces that are heavily impacted by their business models (CHI 2023)

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.