Welcome to the AIology Lab! We are dedicated to advancing the science and application of artificial intelligence and analytics. Our mission is to develop, evaluate, and apply AI-driven methods that support data-informed decision-making across health, business, and social systems.
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Human-Technology Interaction
Research on the reciprocal relationship between humans and artificial intelligence examines both how human behaviors, expectations, and values shape AI systems and how AI systems, in turn, influence human cognition, decision-making, and social interaction. A central focus of this research is anthropomorphism and anthropomorphic AI, which refers to the tendency of humans to attribute human-like traits, intentions, or emotions to AI systems and the deliberate or emergent incorporation of such traits in AI design.
Information Disorder
Research on information disorder examines how false or misleading content, including misinformation and disinformation, is created, spread, and interpreted within human–technology ecosystems, particularly in environments shaped by artificial intelligence and social media platforms. This line of research investigates how human cognitive biases, social influence, and emotional responses contribute to the production and amplification of misleading information, as well as how AI systems such as recommender algorithms, bots, and generative models can unintentionally or strategically accelerate its dissemination.
Health and Medical Informatics
Research on health and medical information leverages text mining and data mining techniques, increasingly augmented by artificial intelligence and machine learning, applied to linked, multi-source data to identify patterns, predict risks, and support evidence-based decision-making. By integrating unstructured text data, such as clinical notes, claims narratives, and social media content, with structured data, including electronic health records, insurance claims, demographic variables, and environmental or neighborhood-level factors, AI-driven models can capture complex, nonlinear relationships and temporal patterns that traditional approaches often miss.
Funding
Our research lab is made possible by the generous support of the following organizations.
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