Applied Research
Applied Research
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Grounded in academic rigor, designed for real-world impact.
I lead and contribute to human-centered research that explores how people interact with intelligent systems—from partially automated vehicles to robotic prostheses. These projects are rooted in peer-reviewed studies and can be translated into practical insights for UX, product, and system design.
As vehicles become increasingly automated, keeping drivers mentally present during the ride is critical for safety. In this study, I set out to test a simple question:
Can in-car prompts help drivers stay engaged—and take back control—when needed?
I ran a simulator-based study to find out, using different types of prompts during automated driving to see what really works. The study directly influenced how we think about designing driver engagement tools that are both effective and unobtrusive.
When designing intelligent lower-limb prostheses, we often ask: How can we make them feel more natural? But the bigger question is: How do users define “natural”—and how do we capture that?
In collaboration with an interdisciplinary team, I helped lead two studies exploring how both non-disabled and amputee users adjust powered prosthesis settings through a process called user-guided auto-tuning.
My focus was to understand the why behind their preferences—what informed their decisions, and how we could design tuning systems that feel more intuitive, empowering, and human.
With the rapid evolution of lower-limb prostheses (LLPs) into intelligent, sensor-driven devices, the interaction between users and their prostheses is no longer passive or mechanical. These systems now offer advanced features like real-time feedback and user-adjustable settings—but that only matters if wearers can understand and use them effectively.
I led this research effort to explore a key challenge:
How do we design human-prosthesis interaction to be intuitive, empowering, and usable—especially as these devices get smarter?
Working with biomedical engineers, I proposed a new framework that helps teams design more transparent and user-centered interactions for intelligent LLPs.
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