About this Event
Join us for an engaging session that highlights how AI and data science can be harnessed for meaningful societal impact.
How do AI Large Language Models Empower Computational Social Science?
Computational Social Science (CSS) increasingly relies on large-scale, multimodal, and high-dimensional data to understand complex social phenomena. However, CSS faces persistent challenges, including unstructured and noisy data, annotation bottlenecks, multimodal integration, and feature-rich inference.
This talk presents a research program on how large language models (LLMs) can empower CSS tasks. It demonstrates how LLM agents address core CSS challenges through real-world case studies: extracting structured knowledge from noisy social media for drug side-effect surveillance, augmenting or automating content annotation for hate and toxicity detection, simulating human-perceived experiences during extreme events through multimodal data fusion of geospatial and socioeconomic signals, and enabling complex decision-making via multi-agent reasoning in business partner selection. Collectively, these examples illustrate how LLMs can transform traditional CSS pipelines from static analysis tools into adaptive, reasoning-driven systems capable of scalable inference and richer modeling of social processes.
Speaker:
Dr. Lingyao Li is an assistant professor in the School of Information at the University of South Florida. He is an interdisciplinary researcher whose work bridges AI, social computing, and domain-specific applications. His research focuses on leveraging crowdsourced data and advanced AI techniques, particularly large language models, to tackle challenges in urban and health informatics. He earned his Ph.D. from the University of Maryland and previously conducted postdoctoral research at the University of Michigan.
Event Details:
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