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
UW NLP Group
The University of Washington Natural Language Processing Group comprises diverse researchers across campus collaborating in the study of all aspects of NLP from computational, engineering, linguistic, social, statistical, and other perspectives.
Behavioral Data Science Group
The Behavioral Data Science Group leverages large-scale behavioral data to extract actionable insights about our lives, health and happiness by combining techniques from data science, social network analysis, and natural language processing.
Allen School Faculty
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Centers & Initiatives
The mission of the UW Center for Research and Education on Accessible Technology and Experiences (CREATE) is to make technology accessible and the world accessible through technology. By bringing together researchers from across the campus, CREATE harnesses the diverse expertise necessary to realize a more just and equitable technological future, one that overcomes existing barriers and ensures new ones do not arise.
The Tech Policy Lab is a unique, interdisciplinary collaboration at the University of Washington that aims to enhance technology policy through research, education, and thought leadership. Founded in 2013 by faculty from the Paul G. Allen School of Computer Science & Engineering, Information School, and School of Law, the Lab aims to bridge the gap between technologists and policymakers and to help generate wiser, more inclusive tech policy.
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.
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.”
Institute for Foundations of Data Science
The International Conference on Artificial Intelligence and Statistics (AISTATS) recognized Jamieson for his 2016 paper underpinning an approach to hyperparameter optimization that has been widely adopted within the machine learning community.