One line of my research focuses on understanding and modeling visual clutter, a major challenge in the design of augmented and mixed reality (AR/MR) systems that overlay virtual information on the real world. We developed and applied computational models to quantify different levels of clutter and examined how clutter impacts attention and task performance. A key insight was the trade-off between overlay clutter, which emerges when information is layered onto a real or virtual scene, and information access effort, which arises when users must scan between separated displays. These findings provide a framework for evaluating how AR interfaces should present information based on attentional and task demands. Importantly, this work informs the design of AI-driven or automated AR systems that can adaptively manage clutter in real-time—for example, by selecting optimal visual layouts or suppressing non-essential information—based on the user’s goals and cognitive load. As AR becomes more deeply integrated into safety-critical operations, intelligent systems that dynamically control visual complexity will be essential for optimizing human performance and building appropriate trust in automation.
My research journey began as an undergraduate at the University of Kansas, where I earned a B.S. in Cognitive Psychology with a minor in Chemistry. After working in a human factors visual information processing lab, I pursued an M.S. in Experimental Psychology at the University of Idaho, followed by an M.F.A. at Washington State University. I then taught courses in psychology and the visual arts at a small university before returning to complete my Ph.D. in Cognitive Psychology with an emphasis in Human Factors Engineering Psychology at Colorado State University. Throughout this journey, my curiosity and interest in understanding human perception, cognition, and technology design have driven my interdisciplinary research path.
The most exciting scientific contribution that I would like to make is advancing a Human–Technology–Environment (HTE) design framework that models the dynamic interplay among human cognitive capabilities and limitations, technology or system behavior, and environmental or task demands. This framework has the potential to inform the design of AI and automation-enabled technologies that align with human performance constraints and operational situations in complex environments.
What most excites me about data and AI research is its potential to meaningfully improve how humans interact with the world—from supporting critical decisions in high-risk domains to improving everyday routines. I’m especially excited by the unique challenges of integrating AI and automation in ways that consider human cognitive capabilities and limitations, adapt to environmental or task demands, and promote ethical, transparent design. The opportunity to contribute to the design of intelligent systems that not only work but work well with people—in real-world, complex contexts—is what makes this field exciting and inspiring.
I have a passion for the visual arts, especially photograph and sculpture, which shapes how I think about human perception, cognition, spatial experience, and their interactions. My background in photography and design plays a role in how I think about research questions related to attention, perception, display design, and emerging technologies.