Allen School researchers are at the forefront of exciting developments in AI spanning machine learning, computer vision, natural language processing, robotics and more.
We cultivate a deeper understanding of the science and potential impact of rapidly evolving technologies, such as large language models and generative AI, while developing practical tools for their ethical and responsible application in a variety of domains — from biomedical research and disaster response, to autonomous vehicles and urban planning.
Groups & Labs
UbiComp Lab
The Ubiquitous Computing (UbiComp) Lab develops innovative systems for health sensing, low-power sensing, energy sensing, activity recognition and novel user interface technology for real-world applications.
Graphics & Imaging Lab (GRAIL)
The work of the Graphics & Imaging Laboratory spans computer graphics, computer vision, generative AI, computational photography, virtual reality, animation and games.
Faculty Members
Faculty
Faculty
Faculty
Faculty
Centers & Initiatives
The Science Hub supports a broad set of programs — including fellowships for doctoral students, collaboration among researchers and support for collaborative research events — designed to accelerate artificial intelligence (AI), robotics and engineering in the Seattle area.
The interdisciplinary DUB group at the University of Washington advances research, collaboration and teaching related to the interaction between design, people, and technology.
Highlights
UW News
In a paper published in the journal Nature, a team of Allen School and Ai2 researchers unveiled OpenScholar, a system that can cite scientific papers as accurately as human experts and incorporate new research after it has been trained.
Allen School News
The Institute of Electrical and Electronics Engineers (IEEE) recognized Kemelmacher-Shlizerman for her “contributions to face, body, and clothing modeling from large image collections,” including pioneering virtual try-on tools and bringing the technology to the mainstream.
Allen School News
A team of Allen School and Ai2 researchers were recognized for developing an efficient, scalable system for indexing petabyte-level text corpora with minimal storage overhead to better understand the data on which large language models are trained.