I am a Ph.D. candidate at the University of Michigan School of Information, working with Libby Hemphill. As a computational social scientist, I study how human behavior and social interaction in online spaces emerge through the interplay of users, social contexts, and technologies. Specifically, my doctoral research focuses on how technological interventions, such as platform-specific features and general-purpose technologies, interact with existing social dynamics to produce different outcomes across user groups in online communities.
Methodologically, I use data science techniques to collect and analyze large-scale digital trace data from diverse online communities. I also completed data science internships at Roblox and Meta. I generate social scientific insights about how users interact with both people and AI in online spaces and translate those insights into evidence-based design recommendations for platforms and practitioners.
News
- Worked as a Data Scientist Intern at Meta on the Sales and Marketing Intelligence team in Menlo Park.
- Presented two papers at ICWSM 2026.
- Presented a poster, “Role Reversals and Role-Based Inequality on Stack Overflow After the Release of ChatGPT,” at CHI 2026.
- Presented my resource paper at CHIIR 2026 in Seattle and joined the virtual doctoral colloquium at iConference 2026.
- Presented my paper on gift contagion among Twitch viewers at IC2S2 2025 in Norrköping, Sweden.
- Worked as a Data Scientist Intern on the Economy team at Roblox.
Recent Publications View all →
The Spread of Virtual Gifting in Live Streaming: The Case of Twitch
Proceedings of the 20th International AAAI Conference on Web and Social Media
Moral Framing and Online Fundraising Outcomes: Evidence from GoFundMe Campaigns
Proceedings of the 20th International AAAI Conference on Web and Social Media