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
Mostafavi Lab
The Mostafavi Lab develops machine learning and statistical methods that combine evidence across multiple types of molecular/genomics data and disentangle spurious from meaningful correlations for new insights into mechanisms of health and disease.
Tsvetshop
Tsvetshop researchers aim to develop practical solutions to natural language processing problems that combine sophisticated learning and modeling methods with insights into human languages and the people who speak them.
Faculty Members
Centers & Initiatives
The Tech Policy Lab is a unique, interdisciplinary collaboration at the University of Washington that aims to enhance technology policy through research, education, and thought leadership. Founded in 2013 by faculty from the Paul G. Allen School of Computer Science & Engineering, Information School, and School of Law, the Lab aims to bridge the gap between technologists and policymakers and to help generate wiser, more inclusive tech policy.
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.
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.