When imagining the future of technology, sometimes all we need to do is look out the window — or into a microscope.
Our researchers take inspiration from nature to redefine what a computer can be, from data storage using synthetic DNA, to sensors modeled on insects and leaves. We also advance technologies to help solve biology’s biggest mysteries, such as computational approaches for understanding the mechanisms of disease and brain-computer interfaces that can restore or augment physical function and mobility.
Research Groups & Labs
Mostafavi Lab
The Mostafavi Lab develops machine learning and statistical methods that combine evidence across multiple types of molecular/genomics data and disentangle spurious from meaningful correlations for new insights into mechanisms of health and disease.
Neural Systems Lab
The Neural Systems Lab at the UW focuses on understanding the brain using computational models and simulations, and applying this knowledge to the task of developing human-like artificial intelligence (AI) and brain-computer interfaces (BCIs).
Faculty Members
Centers & Initiatives
Computing for the Environment (CS4Env) at the University of Washington supports novel collaborations across the broad fields of environmental sciences and computer science & engineering. The initiative engages environmental scientists and engineers, computer scientists and engineers, and data scientists in using advanced technologies, methodologies and computing resources to accelerate research that addresses pressing societal challenges related to climate change, pollution, biodiversity and more.
The Center for Neurotechnology (CNT) got its start in 2011 as one of several Engineering Research Centers (ERCs) funded by the National Science Foundation. CNT is headquartered at the University of Washington, with core partners at the Massachusetts Institute of Technology and San Diego State University. CNT researchers focus on developing and applying principles of engineered neuroplasticity to revolutionize the treatment of spinal cord injury, stroke and other debilitating neurological conditions.
Highlights
Allen School News
From a robotic arm that learns to pick up new objects in real time, to a model that converts 2D videos into 3D virtual reality, to a curious chatbot that adapts to users, to machine learning methods for decoding the brain, the 2025 Research Showcase and Open House had something for everyone.
UW News
In an article in Nature Reviews Bioengineering, members of the AIMS Lab led by Allen School professor Su-In Lee discuss how explainable AI techniques are essential for ensuring accuracy and trust in AI models used in clinical settings.
Allen School News
In a recent paper, a team of researchers led by professor Matt Golub designed a new machine learning technique to understand how different parts of the brain talk to each other even when some parts can’t be directly observed.