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
WEIRD Lab
The Washington Embodied Intelligence and Robotics Development lab is interested in robotics problems, and currently we are thinking deeply about reinforcement learning algorithms to enable real-world robotic manipulation tasks in the home.
H2 Lab
The H2 Lab addresses foundational problems in Artificial Intelligence and Natural Language Processing to develop general-purpose AI algorithms that represent, comprehend, and reason about diverse forms of data at large scale.
Allen School Faculty
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Associate Professor
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.
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.
Highlights
Allen School News
In December, Feng was named among the 2026 class of NVIDIA Graduate Fellows in recognition of his work on model collaboration, where “multiple AI models, trained on different data, by different people, and thus possess diverse skills and strengths, collaborate, compose and complement each other.”
Institute for Foundations of Data Science
The International Conference on Artificial Intelligence and Statistics (AISTATS) recognized Jamieson for his 2016 paper underpinning an approach to hyperparameter optimization that has been widely adopted within the machine learning community.
Allen School News
Multiple Allen School authors received Best Paper Awards or honorable mentions for their work on interactive systems that enable more flexible human-AI agent collaboration, an AI-based tool that helps screen-reader users make sense of geovisualizations, and more.