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
Robot Learning Lab
The Robot Learning Lab works on foundational research in machine learning, AI and robotics to develop intelligent robotic systems that can perceive, plan and act in complex environments and improve performance with experience.
Personal Robotics Lab
Our mission is to develop the fundamental building blocks of perception, manipulation, learning, and human-robot interaction to enable robots to perform complex physical manipulation tasks under clutter and uncertainty with and around people.
Allen School Faculty
Associate Professor
Professor
Associate Professor
Assistant Professor
Centers & Initiatives
Society + Technology is a cross-campus, cross-disciplinary initiative and community at the University of Washington that is dedicated to research, teaching and learning focused on the social, societal and justice dimensions of technology.
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.
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.