Lap Fai Yu
George Mason University, VA·Computer Science
Training firefighters and rescue workers is risky, and realistic practice scenarios are hard and costly to stage. Mixed reality (MR), which overlays virtual objects onto a person's view of the real world through a headset, offers a safer option.
- Fields
Computer Science
Engineering
- Topics
mixed reality
augmented reality
virtual reality
- Grants
- 1 known grant, through Jun 2028
What they work on
- Investigate how artificial intelligence techniques can be integrated with mixed reality headsets to assist first responders during tasks such as firefighting and search and rescue.
- Devise an optimization-based generative framework that automatically adapts mixed reality training scenarios, with virtual fires, hazards, robots, and victims, to the layout of real-world scenes.
- Conduct user studies with first responders, in partnership with the Fairfax, Virginia Fire and Rescue Department, to evaluate performance gains from the MR interfaces and synthesized training scenarios.
More about the research
A trainee standing in a real room could face a virtual fire, hazards, teammates, robots, and people who need rescue. The challenge is building training scenarios that fit any real space and that teach responders to work alongside artificial intelligence (AI).
Why it matters
Firefighters and other first responders face injury and death risks even during training. Mixed reality scenarios fitted to real buildings, combined with AI assistance, could give them safer and more varied practice in firefighting and search and rescue, and could help reduce training-related risks and casualties.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Haeyong Chung
University of Alabama in Huntsville, AL·Computer Science·Early-career
Analyze existing multi-display applications in the academic literature to derive design knowledge on how multiple displays and devices can form one coherent visualization system for visual analysis.
Rui Yu
University of Louisville, KY·Computer Science
Extract a labeled structural skeleton from single noisy images by fitting geometric primitives such as planes and cylinders to monocular depth estimates, using large vision-language models to assign meaning.
Malihe Alikhani
Northeastern University, MA·Computer Science
Build an AI tool that recognizes ASL signs used in classroom conversation and shows related scientific terms in real time through augmented reality, signing avatars and English captions.
The school
George Mason University: 15 labs on PI Pages, $282M a year on research, 297 research doctorates a year.
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