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
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.
Quantum Group
The Quantum Group does research on a variety of topics in quantum information and computation (primarily on the theory side), including quantum complexity theory, error-correction, cryptography, algorithms, and learning.
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 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.
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.