Daniel Killough PhDx’28 Is Designing for Everyone  

For Daniel Killough, accessible technology is personal. Since high school, he has been drawn to extended reality (XR) devices and the question of who gets to use them — and who gets left out. Now a PhD student at UW–Madison, he is driven to ensure that emerging devices like augmented reality (AR) and virtual reality (VR) headsets are built for everyone, and that their potential as the next generation of computing is available to all.

(In Their Own Words is a series featuring UW-Madison Computer Sciences graduate students sharing their research and offering a firsthand look at how their work connects to real-world challenges and the Wisconsin Idea.)  

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I’m Daniel Killough, a third-year PhD student in Computer Sciences working as a Human-Computer Interaction research assistant in Dr. Yuhang Zhao’s MadAbility Lab. My research focuses on developing technology that is more accessible for people with disabilities, particularly making extended reality devices like the Meta Quest or Apple Vision Pro usable for blind and low-vision people. I believe hands-free smart devices like these will become the next generation of computing, but right now many head-worn products require that you can see in order to interact with them, shutting out millions of potential users.  

My interest in making technology more accessible started with my dad. He’s always had issues with the software he needs to use every day. (Don’t get him started on Microsoft Word vs WordPerfect!) He grew up with poor eyesight, and I’ve noticed my own vision starting to change. With computers now essential to everyday life, I kept wondering: Why do so many systems seem to be designed without testing among the people who actually use them?  

This question left me thinking about a broader issue: Disability can happen to anyone at any time. Accessible solutions should be readily available for anyone whenever that need arises. If someone can no longer use the technology required for their job — due to confusing interfaces, vision loss, or a situational or temporary impairment — they’ll need accessible solutions to maintain their position.  

Starting in high school and during my undergraduate studies at The University of Texas at Austin, I worked on various user-centered systems and research projects for accessibility and social impact. For example, one of our projects featured a mobile augmented reality (AR) app that teaches young adults about the cosmetic dangers of skin cancer by visualizing sun damage on their own faces. Our study showed that players who used our app demonstrated significantly improved intentions to use sun protection — and convince their friends and family to do the same — compared to a control group.  

Here at UW–Madison, my most recent project is VRSight, which uses AI to recognize objects in any VR application like signs, seating areas, or other people. This system works much like a traditional screen reader, but sounds come from the direction where objects exist in the virtual space. For example, if there’s a whiteboard with important formulas on it to your left, you hear the board and its contents described from your left. We also provide auditory feedback when users approach the edge of their physical play space, using a caution sound from the direction of the boundary. And to keep players immersed, we customize the AI-generated speech’s tone of voice to match the feel of the app being used.  

In our testing, VRSight enabled blind users to complete tasks in virtual spaces without sight, like reading text on posters or finding an open seat at a virtual conference table. Critically, our system works on any VR application without waiting for developers to build accessibility features into each of their apps — though we can certainly improve the quality if they do — hopefully enabling VR and mixed reality devices to be used by people with visual impairments.  

To make VRSight work, we also created a dataset of over 17,000 images called DISCOVR, which other researchers can use for their own AI and virtual reality projects. I mentored three undergraduate students: Justin Feng, Zheng Xue Ching, and Daniel Wang. They helped me complete the project, each taking real ownership over different parts of the system. Staying true to the Wisconsin Idea, we’ve made everything freely available, so this work doesn’t stay only in the lab: VRSight’s code, DISCOVR’s dataset, and our open-access papers are all available for other developers and researchers to download and build on.  

Looking forward, future versions of VRSight could add navigation assistance for objects, with better support for moving through real-world environments. I envision extensions of VRSight that complement existing accessibility features, like tactile strips on sidewalks or auditory crossing warnings at traffic lights, with real-time spatial audio descriptions in smart glasses for people navigating outdoor environments. This summer, I’ll be joining Google as a student researcher in San Jose working with the Maps team, and I hope to take the principles I’ve learned there to impact products at scale.  

That thinking extends beyond the lab in other ways, too. As part of my PhD, I’m pursuing a distributed minor in “Breadth of Foreign Language and Culture,” studying one semester each of Mandarin Chinese, German, and American Sign Language, complementing past studies in Spanish and Japanese. Each language offers me a different way to think about how people communicate and connect with one another, and how I should tailor and localize my systems in ways that are most useful to people with different backgrounds.  

I don’t believe my dad’s frustrations with usability are unique to him, and similarly VR accessibility challenges aren’t unique to blind users. Every time we design technology that excludes people with disabilities, we miss opportunities to gain greater insights into their lived experiences and normalize accessible design as the default. I’m excited to keep working on systems that include and inspire others — through AR and VR, the next generation of (AI-driven?) computing platforms, and whatever comes next.  

 Edited by Karen Barrett-Wilt