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
Robot Learning Lab
The Robot Learning Lab works on foundational research in machine learning, AI and robotics to develop intelligent robotic systems that can perceive, plan and act in complex environments and improve performance with experience.
Robotics and State Estimation Lab
We are interested in the development of computing systems that interact with the physical world in an intelligent way. To investigate such systems, we focus on problems in robotics and activity recognition.
Faculty Members
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.
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
Winners Andrew Alex and Megan Frisella aim to advance research in user-scheduled programming languages, while fellow Allen School winner Zixian Ma and UW ECE collaborator Yushi Hu will develop multi-modal AI agents capable of performing complex tasks.
UW News
In an article in Nature Reviews Bioengineering, members of the AIMS Lab led by Allen School professor Su-In Lee discuss how explainable AI techniques are essential for ensuring accuracy and trust in AI models used in clinical settings.
Allen School News
To help Reddit moderators make data-driven decisions on what rules are best for their community, a team of researchers in the Allen School’s Behavioral Data Science Group and Social Futures Lab conducted the largest-to-date analysis of over 67,000 Reddit rules and their evolution.