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 communication and process for obtaining consent is deeply related to to addressing a wide range of 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, such as technology-mediated abuse. 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 opt into giving companies my data?"). 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 that better protect users’ consent boundaries. Examples include:

• building systems that let users precisely and flexibly define one's consent boundary per post (instead of centering a space or network); e.g., a system for facilitating information sharing among PhD students about mistreatment or abuse in academia—in a way so that each student's consent boundary is respected (in preparation for submission, CHI LBW 2024)
• designing privacy interfaces that help users make less burdensome and more informed data-related consent decisions (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. My advisor is Professor Kentaro Toyama. I am a Meta Research PhD Fellow (selected on my fourth try) and a Barbour Scholar. My research has also been recognized with/by EECS Rising Star, honorable mention awards from ACM CHI and ACM WebSci, Heidelberg Laureate Forum Young Researcher, and the Consortium for the Science of Sociotechnical Systems. My research gave practical help to founders of new social media and was covered by The Wall Street Journal. I also played a key role in winning an unrestricted gift ($75,000) from Google (PI: Professor Florian Schaub). I am on the academic job market for 2024-2025.