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
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.
Molecular Information Systems Lab (MISL)
MISL explores the intersection of information technology and molecular biology using in-silico and wet lab experiments, drawing upon expertise from computer architecture, programming languages, synthetic biology and biochemistry.
Faculty Members
Centers & Initiatives
Center for the Future of Cloud Infrastructure (FOCI)
The UW Center for the Future of Cloud Infrastructure (FOCI) aims to foster a tight partnership between practitioners and researchers in both industry and academia to define the next generation of cloud infrastructure to achieve new levels of security, reliability, performance along with cost-efficiency and environmental sustainability.
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.
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.