Research Interests

My research sits at the intersection of network science, machine learning, and human communication. I study how social structure, language, and emotion interact to shape collective behavior in large, connected systems.

Network Structure & Collective Patterns

I study how large social systems organize themselves. Using tools from network science, I examine how patterns like clustering, fragmentation, densification, and reorganization emerge from many individual interactions. These structural properties shape what kinds of coordination, disagreement, or stability are possible in a system.

Dynamics of Communication & Emotion

I analyze how communication changes over time as people interact. Language carries emotion, intent, and perspective, and these elements influence how messages spread and how groups respond. By modeling emotional and linguistic signals within networks, I study how collective moods, escalation, and convergence develop.

Discourse, Meaning, & Social Alignment

I focus on how meaning is constructed in interaction. Using large-scale text analysis and natural language processing, I study how narratives, moral language, and framing evolve across communities. This work helps explain how shared understanding forms, how misalignment grows, and how divisions harden or soften.

Computational Models of Social Systems

I build and apply computational models that integrate network structure with machine learning. These models are designed to handle ambiguity, context, and diversity in communication, allowing me to study complex social behavior without reducing it to simple categories. My goal is to develop tools that help us observe social systems as they change and identify early signals of instability or transformation.