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. Consent is deeply related to tackling a range of problems around power, safety, and privacy. For example, companies collect and share user data without users even knowing—there is often no real consent. It is also difficult to pick and choose—to give consent to—who can see one’s social media posts, given platforms' impoverished privacy settings. Users from marginalized groups face additional risks, such as online harassment.
To develop solutions to address such problems, understanding what consent is critical. My research has advanced the theory of affirmative consent by normatively defining its properties—to rethink the role of consent in the field of computing, and to reimagine social platforms. Based on this theory, I design and build 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 those with less power, without knowing each other's identities (in submission), consent mechanisms for AI-based 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.
- Below is a list of recent updates, which admittedly focuses on good news. I think it's important to share bad news as well, to normalize vulnerabilities, rejections, and failures. While it's far from perfect, you can glimpse more of my honest thoughts on this page.
- 10/22/2024: I discussed research, mentoring, feminism, and what drew me to UMSI -- all in this interview with Abigail Mcfee, who's a wonderful interviewer and writer.
- 09/13/2024: Finished the last chapter of my dissertation. I learned a ton during the process -- I want to write a blog post about it at some point, so that I don't forget it all! I also did a short interview with Heidelberg Laureate Forum Foundation about my research and path to HCI.
- 08/24/2024: Another paper is accepted to Privacy Enhancing Technologies Symposium. Congrats to the team, especially Byron, Sean, and Florian! (I like this paper's method.)
- 08/01/2024: A paper with Lu Xian, Song Mi Lee-Kan, and Florian Schaub is accepted to PoPETs 2025. Congrats to all authors and many thanks to Florian for the guidance!
- 05/21/2024: Relieved and happy to say we successfully hosted the panel on PhD advising at CHI! Other updates -- I'll be traveling in June to attend CSST. I was also selected to attend the Heidelberg Laureate Forum.
