projects
Our lab's work is grouped into four broad tracks which make up the primary research programs at the Computational Social Science Lab.
Studies of Large Language Models
Investigating the safety, behavior, and societal impact of large language models through red teaming, auditing, and experimental evaluation.
LLM Red Teaming & Safety
Adversarial testing and safety evaluation of large language models
Agentic AI Persuasions
Understanding AI agents' persuasive capabilities in realistic social settings
LLM-Mediated Information Systems
How large language models reshape information exposure and consumption
Studies of Sociotechnical Systems
Examining how algorithmic systems, human behavior, and platform design interact to shape online experiences and societal outcomes.
Auditing Sociotechnical AI Systems
Comprehensive evaluation of AI systems in real-world deployment contexts
Counterfactual Auditing Methods
Causal inference frameworks for auditing algorithmic systems
Cross-Platform Comparative Studies
Understanding systemic patterns across different platform architectures
Studies of Information Ecosystems
Understanding how information flows, fragments, and influences society through traditional and digital media platforms.
Our Shared Reality
Information ecosystem - mainstream
Media Fragmentation Analysis
Cross-platform information consumption patterns
Computational Methods
Developing novel computational and statistical methods for studying complex sociotechnical systems.
Statistical Inference on Networks
Developing robust measurement frameworks for online discourse
Quantifying Concepts
Measuring Partisanship, Factual Accuracy, and Extremism in Media Discourse