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
Sensor Systems Laboratory
The Sensor Systems Laboratory invents new sensor systems, devises new ways to power and communicate with them, and develops algorithms for using them, with applications in the domains of bioelectronics, robotics, and ubiquitous computing.
H2 Lab
The H2 Lab addresses foundational problems in Artificial Intelligence and Natural Language Processing to develop general-purpose AI algorithms that represent, comprehend, and reason about diverse forms of data at large scale.
Allen School Faculty
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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 AI Institute for Societal Decision Making (AI-SDM) brings together AI and social sciences researchers to develop human-centric AI for societal good that harnesses the power of data and improved understanding of human decisions to create better and more trusted choices.
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.