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. Method-wise, I 1) develop theory or conduct empirical studies to reimagine social computing systems, 2) build systems, and 3) evaluate them using field studies and experiments.

The key idea behind my research is that the idea of consent is deeply related to to various socio-technical problems. Current technologies enable or exacerbate two classes of problems that negatively impact society. The first is interpersonal harm people cause one another—this includes both drastic ones, like technology-mediated abuse, and more subtle, but still harmful dynamics. The second is institutional exploitation of users, especially with the rise of AI, such as companies’ invasive tracking and inferences of user information. Both are closely related to people’s consent. E.g., "Do I decide to interact with this user?", "Do I feel comfortable with companies learning this about me?" Thus, consent is an important concept to define for software and policy design.

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. Examples include:

• building systems that enable risky communication by letting users flexibly define each post's consent boundary (i.e., audience), instead of centering a space or network; e.g., a system for facilitating discussions among PhD students about PhD advising relationships—in a way so that users' boundaries are respected (in submission, CHI LBW 2024)
• designing interfaces that help users make data privacy decisions closer to their affirmative consent (e.g., findable ad settings (CHI 2023), consent interfaces for AI-based inferences (CHI 2024))
• rethinking social media’s business models—because it is fundamental to how companies obtain users' consent (ongoing)

I am a Ph.D. candidate at the University of Michigan School of Information and Department of Computer Science & Engineering, where I am advised by Professor Kentaro Toyama. My research has been recognized with/by a Meta Research PhD Fellowship (selected on my fourth try), University of Michigan Barbour Scholarship, EECS Rising Star, honorable mention awards from ACM CHI and ACM WebSci, and the Consortium for the Science of Sociotechnical Systems. My work gave practical help to founders of new social media and was covered by The Wall Street Journal. I am on the academic job market for 2024-2025.