Research
I work at the intersection of computational social science and human-AI interaction. My work combines causal inference, large-scale experiments, and human-centered design, with a recurring focus on heterogeneous effects: who benefits from an intervention, who does not, and why.
How network structures, algorithmic curation, and platform policies shape user behavior—and how to design socio-technical mechanisms that return meaningful control to users.
- Wikipedia newcomer mentorship — Causal analysis of 35K+ first conversations showing a mentor's reply raises retention, most for technical questions. → ICWSM 2026
- User agency on social media — Heterogeneity analysis of a six-month feed-ranking field experiment, plus a Bluesky tool that lets users personalize feeds toward their own goals.
How interactive AI acts as an external cognitive scaffolding for human reasoning, and how to build systems that support deliberation, perspective-taking, and active participation while preserving independent agency.
- AI and future thinking — Operationalizing "future thinking" (prospection) in human-AI dialogue and detecting it at scale across large conversation datasets.
- Human-AI co-writing & attitude change — A multi-agent co-writing system tested in a pre-registered RCT on whether writing a first-person scenario shifts attitudes toward unfamiliar social issues.
- AI scaffolding for participation — Lookover, a tool helping students join peer discussion while keeping the thinking their own. → UIST 2026
- Simulating user response — Early work on modeling user cognition and behavior from minimal data, which informs how I now use model-generated agents in experimental designs. → CogSci 2024
How information propagates across complex relational structures, and how algorithmic foundations can model network topologies while preserving node distinctiveness and heterogeneity.
- Graph neural networks — DDSM, reframing message passing to mitigate over-smoothing, benchmarked against 15 baselines. → SIGKDD 2025