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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 person with long blond hair, with only mouth and chin visible, is lying on a blue quilted blanket on short green grass in dappled sunlight. The person is wearing a black sweatshirt and propped up on their elbows, viewing a smartphone held in their well-manicured hands.

Behavioral Data Science Group

The Behavioral Data Science Group leverages large-scale behavioral data to extract actionable insights about our lives, health and happiness by combining techniques from data science, social network analysis, and natural language processing.

Young man adjusting the position of robotic arm while students watch.

Robotics Group

Doing ground-breaking work in mechanism design, sensors, computer vision, robot learning, Bayesian state estimation, control theory, numerical optimization, biomechanics, neural control of movement, computational neuroscience, brain-machine interfaces, natural language…


Faculty Members

Faculty

Faculty

Faculty


Centers & Initiatives

Change is a cross-campus collaboration that explores the challenges of developing technology in the context of positive social change. It seeks to make connections between researchers, outside organizations, and the public to inspire the development of new capabilities aligned with the interests of those most in need.

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.

Highlights


Allen School News

Allen School Ph.D. student Cheng-Yu Hsieh is interested in tackling one of the biggest challenges in today’s large-scale machine learning environment — how to make AI development more accessible. His research focuses on making both data and model scaling more efficient and affordable.

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.

Allen School News

Sharma (Ph.D., ‘24) won the 2024 award from the Association for Computing Machinery for leveraging AI to make high-quality mental health support more accessible, and Min (Ph.D., ‘24) received an honorable mention for developing a new class of efficient and flexible language models.