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
Security and Privacy Research Lab
The Security and Privacy Research Lab works on a variety of topics, ranging from studying and addressing security and privacy risks in existing technologies, to anticipating future risks in emerging technologies.
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
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.
IFDS organizes its research around four core themes: complexity, robustness, closed-loop data science, and ethics and algorithms. By making concerted progress on these fundamental fronts, IFDS aims to lower several of the barriers to better understanding of data science methodology and to its improved effectiveness and wider relevance to application areas.
Highlights
Allen School News
Cardinality estimation helps guide decisions on every aspect of query execution, but current methods often have large errors. To address this, Suciu introduced a more accurate and efficient cardinality estimator, LpBound, which provides a guaranteed upper bound on the query output size.
Allen School News
Balazinska was elected to the WSAS, which provides scientific and technical advice to state policymakers, based on her “contributions in data management for data science, big data systems, cloud computing, and image/video analytics and leadership in data science education.”
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.