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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…


Allen School Faculty

Professor

Assistant Professor


Centers & Initiatives

The Institute for Medical Data Science (IMDS) is a joint effort among the Schools of Medicine and Public Health and the College of Engineering, including the Allen School to lead the development and implementation of cutting-edge AI and data science methods in medical data science. By harnessing the power of AI across diverse health determinants, IMDS aims to improve patient health, provider satisfaction, and healthcare operations, particularly in the Pacific Northwest region.

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


Allen School News

In the latest installment of a Q&A series exploring grand challenges in computing, professors Franziska Roesner and Sewoong Oh discuss how they and their colleagues aim to develop a safer, more secure and privacy-preserving future through technologies that better serve people and society.

UW News

Allen School Ph.D. student Yael Eiger and professor Franziska Roesner discuss their research examining people’s experiences with technology during and after incarceration and the broader impacts of technology used for policing and post-release monitoring.

Allen School News

Professors Andrea Coladangelo, Matthew Golub, Ranjay Krishna, Chinmay Nirkhe, and Stephanie Wang are shaping the future of computing through projects spanning AI, systems, computational neuroscience, cryptography and quantum computing.