We study how false and misleading content spreads on social media — from coordinated inauthentic behaviour and state censorship to AI-generated deepfakes and synthetic media. We build detection methods and characterize the impact of these threats on public discourse.

We study how people interact with AI assistants in high-stakes and sensitive domains — from romantic relationships and mental health support to legal and medical advice. A central question is how LLMs handle disagreement: when they defer, push back, or subtly steer a user's views. This connects to broader questions of epistemic authority — how AI systems shape what people believe, how they reason, and whom they trust.

We apply computational methods to understand large-scale social phenomena — including cross-partisan dynamics, political discourse across platforms, gender in online communication, and the methodological challenges of studying social media data.

We explore how LLMs and multi-agent AI systems can accelerate scientific workflows and transform education — from automated opinion mining and structured election analysis to orchestrating agent collaborations that produce full data science research papers end-to-end. We are also building AI-assisted teaching tools and studying the epistemic implications of delegating scientific reasoning to generative models.