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—affirmative consent is voluntary, informed, revertible, specific, and unburdensome (CHI 2021). Based on such theory, I create and evaluate systems and interfaces. Concretely, my directions include:
- theorizing and building systems 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 (CHI LBW 2024, in submission), consent mechanisms for AI-based inferences (CHI 2024)
- rethinking 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 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.
