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
Bespoke Silicon Group
The Bespoke Silicon Group aims to bring hardware design to its highest art and rapidly conceive of, design and implement entirely new kinds of hardware faster than has ever been done before.
Tsvetshop
Tsvetshop researchers aim to develop practical solutions to natural language processing problems that combine sophisticated learning and modeling methods with insights into human languages and the people who speak them.
Allen School Faculty
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Centers & Initiatives
TCAT harnesses the power of open-source technology to develop, translate, and deploy accessible technologies, and then sustain them in the hands of communities. Housed by the Paul G. Allen School for Computer Science & Engineering, TCAT centers the experience of people with disabilities as a lens for improving design & engineering, through participatory design practices, tooling and capacity building.
The interdisciplinary DUB group at the University of Washington advances research, collaboration and teaching related to the interaction between design, people, and technology.
Highlights
Allen School News
Professor Magda Balazinska was honored for her influential contributions in data management and data science, while Professor Shwetak Patel was recognized for his groundbreaking work applying computing to health and sustainability.
Forbes
Kim was honored in the health care and sciences category for his work with professor Su-In Lee in the Allen School’s AI for bioMedical Sciences (AIMS) Lab on methods for improving the transparency, safety and explainability of medical AI systems.
Allen School News
In December, Feng was named among the 2026 class of NVIDIA Graduate Fellows in recognition of his work on model collaboration, where “multiple AI models, trained on different data, by different people, and thus possess diverse skills and strengths, collaborate, compose and complement each other.”