Our researchers are driving innovation across the entire hardware, software and network stack to make computer systems more reliable, efficient and secure.
From internet-scale networks, to next-generation chip designs, to deep learning frameworks and more, we build and refine the devices and applications that individuals, industries and, indeed, entire economies depend upon every day.
Research Groups & Labs
Database Group
The Database Group advances both theoretical and systems work in probabilistic databases, stream processing, sensor-based monitoring, databases and the web, XML, image/video data management, data management for machine learning, data mining and more.
SAMPL
SAMPL is an interdisciplinary machine learning research group exploring problems across the system stack, including deep learning frameworks, specialized hardware for training and inference, new intermediate representations and more.
Faculty Members
Centers & Initiatives
NSF AI ACTION Institute
The NSF AI Institute for Agent-based Cyber Threat Intelligence and Operation (ACTION) seeks to change the way mission-critical systems are protected against sophisticated, ever-changing security threats. In cooperation with (and learning from) security operations experts, intelligent agents will use complex knowledge representation, logic reasoning, and learning to identify flaws, detect attacks, perform attribution, and respond to breaches in a timely and scalable fashion.
Society + Technology
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.
Highlights
Allen School News
Deeds introduced partition constraints, a new approach for making conjunctive query executions more efficient. He presented the research at the 28th International Conference on Database Theory (ICDT), earning both the Best Student Paper and Best Paper Awards.
Allen School News
A team of Allen School researchers introduced computational illusion knitting — a design framework that helps automate the process, making illusion knitting more accessible and allowing for more complex and multi-view patterns like hidden Mona Lisas that were previously believed to be impossible.
Agents of Tech
Nivala, co-director of the Molecular Information Systems Lab (MISL), discusses the groundbreaking potential of DNA-based data storage and its role in the future of AI.