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 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, and evaluate them by conducting field studies and experiments. Concretely, my directions include:
- theoretizing and building social platforms that center users' consent boundaries (CHI 2021, CHI LBW 2024, in submission); e.g., Moa, a platform for enabling discussions among PhD students navigating challenging advising relationships—so that users have precise control over their posts' audience, instead of rigidly centering a particular space or network
- developing data and algorithm interfaces that help users make privacy decisions closer to their affirmative consent (CHI 2023, 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 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.
- 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.
- 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.
