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
Makeability Lab
The Makeability Lab specializes in Human-Computer Interaction and applied machine learning for high-impact problems in accessibility, computational urban science, and augmented reality.
Database Group
The UW Database Group does theoretical, systems and user-centered work in multimodal database management systems; generative AI and data management; complexity of query evaluation and optimization; scalable, interactive data visualization; and more.
Allen School Faculty
Assistant Professor
Professor
Associate Professor
Assistant Professor
Centers & Initiatives
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.
The Science Hub supports a broad set of programs — including fellowships for doctoral students, collaboration among researchers and support for collaborative research events — designed to accelerate artificial intelligence (AI), robotics and engineering in the Seattle area.
Highlights
Allen School News
Professor Magda Balazinska was honored for her influential contributions in data management and data science, while Professor Shwetak Patel was recognized for his groundbreaking work applying computing to health and sustainability.
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.