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.
Sampa
Sampa is an interdisciplinary computer architecture group whose research crosses multiple layers of the system stack, from hardware to programming languages and applications, motivated by new device technologies and applications.
Faculty Members
Centers & Initiatives
MEM-C is a NSF Materials Research Science and Engineering Center that integrates materials innovations with theory and computation to advance spin-photonic nanostructures and elastic layered quantum materials, aided by an “AI Core” that integrates artificial intelligence-driven materials discovery.
The eScience Institute empowers researchers and students in all fields to answer fundamental questions through the use of large, complex, and noisy data. As the hub of data-intensive discovery on campus, we lead a community of innovators in the techniques, technologies, and best practices of data science and the fields that depend on them.
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.