Dr. Yun Wan

Yun Wan is Associate Professor of Information Systems and Research Coordinator at the Marilyn Davies College of Business, University of Houston–Downtown, and holds a Ph.D. in MIS from the University of Illinois at Chicago. His work on generative AI, digital platforms, and human–AI collaboration appears in MIS Quarterly Executive, Electronic Markets, Information & Management, IEEE Computer, and IEEE Internet Computing, with coverage in Nature. He has co-led over $2.5M in NSF-funded research.

Topic Focus: Monoculture in the Loop: Persona Diversity as a Defense Against Correlated Blind Spots Generative AI is being absorbed into threat modeling, red-teaming, and code review faster than we have examined what it does to the variance of human judgment. Evidence from human–AI collaborative ideation shows that a shared model with a default persona narrows the space of ideas a team produces — participants converge. In a security context that convergence is not a productivity question but an exposure question: if every reviewer is steered toward the same hypotheses, the residual attack surface becomes systematic rather than random, and it is shared across every organization using the same model. This talk presents the homogenization effect, the persona-diversification intervention that mitigates it, and what deliberately heterogeneous AI reasoning would look like in red-team and secure-design workflows.