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Artificial Intelligence

Allen School researchers are at the forefront of exciting developments in AI spanning machine learning, computer vision, natural language processing, robotics and more.

We cultivate a deeper understanding of the science and potential impact of rapidly evolving technologies, such as large language models and generative AI, while developing practical tools for their ethical and responsible application in a variety of domains — from biomedical research and disaster response, to autonomous vehicles and urban planning.


Groups & Labs

A robot playing table tennis with human partner

Social RL Lab

The Social RL Lab aims to leverage social information in human-AI and multi-agent interactions to enable AI to learn complex behavior, rapidly adapt to new circumstances and cooperate to achieve joint goals—similarly to how humans and animals learn.

Neural Systems Lab featured photo with an activated brain.

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

Adjunct Faculty

Emeritus Faculty


Centers & Initiatives

CS 4 the Environment logo

Computing for the Environment (CS4Env)

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.

IFDS logo in multi-colored block letters with graphic of neuron connections and wording underneath Institute for Foundations of Data Science

Institute for Foundations of Data Science (IFDS)

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