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
Sensor Systems Laboratory
The Sensor Systems Laboratory invents new sensor systems, devises new ways to power and communicate with them, and develops algorithms for using them, with applications in the domains of bioelectronics, robotics, and ubiquitous computing.
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
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.
Computing for the Environment (CS4Env) at the University of Washington supports novel collaborations across the broad fields of environmental sciences and computer science & engineering. The initiative engages environmental scientists and engineers, computer scientists and engineers, and data scientists in using advanced technologies, methodologies and computing resources to accelerate research that addresses pressing societal challenges related to climate change, pollution, biodiversity and more.
Highlights
Allen School News
Allen School Ph.D. student Cheng-Yu Hsieh is interested in tackling one of the biggest challenges in today’s large-scale machine learning environment — how to make AI development more accessible. His research focuses on making both data and model scaling more efficient and affordable.
Allen School News
A team of University of Washington and NVIDIA researchers developed FlashInfer, a versatile inference kernel library that can help make large language models faster and more adaptable, and received a Best Paper Award at MLSys 2025 for their work.
Allen School News
Sharma (Ph.D., ‘24) won the 2024 award from the Association for Computing Machinery for leveraging AI to make high-quality mental health support more accessible, and Min (Ph.D., ‘24) received an honorable mention for developing a new class of efficient and flexible language models.