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
RAIVN Lab
The Reasoning, AI, and VisioN (RAIVN) Lab directed by Prof. Ali Farhadi and Prof. Ranjay Krishna focuses at the intersection of computer vision, machine learning, natural language processing and robotics and is targeted towards helping computers…
Social RL Lab
The Social RL Lab aims to leverage social information in human-AI and multi-agent interactions to enable AI to learn complex behavior, rapidly adapt to new circumstances and cooperate to achieve joint goals—similarly to how humans and animals learn.
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 Community-Engaged Computing Initiative (CECI) is a joint initiative to support community-centered scholarship and research within the broad computing and information field. Co-led by the Paul G. Allen School of Computer Science & Engineering, the Department of Human Centered Design & Engineering, and the Information School, the initiative was launched in 2025 through a gift from Google. CECI supports projects that bring UW faculty and graduate students together with community partners to bring sustainable, equitable, and inclusive technology into real-world contexts.
Highlights
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.
Allen School News
Multiple Allen School authors received Best Paper Awards or honorable mentions for their work on interactive systems that enable more flexible human-AI agent collaboration, an AI-based tool that helps screen-reader users make sense of geovisualizations, and more.