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
SAMPL
SAMPL is an interdisciplinary machine learning research group exploring problems across the system stack, including deep learning frameworks, specialized hardware for training and inference, new intermediate representations and more.
AIMS Lab
The AI for bioMedical Sciences (AIMS) Lab fundamentally advances the way AI is integrated with biology and clinical medicine by addressing novel scientific questions spanning explainable AI, model auditing, disease drivers, and more.
Allen School Faculty
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Centers & Initiatives
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.
RAISE envisions a future where AI systems are developed and used in alignment with human ethics and values. With researchers from over a dozen labs across disciplines, RAISE is a leading center for research and education: building, evaluating, and envisioning AI technologies in the area of Responsible AI.
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.
Forbes
Kim was honored in the health care and sciences category for his work with professor Su-In Lee in the Allen School’s AI for bioMedical Sciences (AIMS) Lab on methods for improving the transparency, safety and explainability of medical AI systems.
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.”