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Chenyu You

Chenyu You

Assistant Professor advancing AI for health and trustworthy machine intelligence

Stony Brook, New York
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Summary

Chenyu You is an Assistant Professor at Stony Brook University, specializing in Data Science within the Departments of Applied Mathematics & Statistics and Computer Science. His core research revolves around 'AI for health,' focusing on developing machine learning methods that learn from biomedical data, reason about clinical contexts, and contribute to healthcare advancements. He is affiliated with the CVLab, AI Institute, and Institute for Advanced Computational Science (IACS). chenyuyou+3
His academic background is strong in Electrical Engineering, with a Ph.D. from Yale University (2024), an M.S. from Stanford University (2019), and a B.S. from Rensselaer Polytechnic Institute (2017). His doctoral research at Yale, advised by James S. Duncan, explored principles and practices of machine intelligence for reliable and trustworthy AI, particularly in healthcare. chenyuyou+3
Chenyu has made significant contributions to the field of medical image analysis and machine learning, evidenced by numerous publications in top-tier venues like Nature Communications, IEEE Transactions on Medical Imaging (TMI), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), NeurIPS, ICML, ICCV, and MICCAI. He has received recognition as a 'World's Top 2% most-cited scientist' by Stanford University since 2024 and is a Platinum Distinguished Reviewer for IEEE TMI. chenyuyou+3
His research specifically aims to address challenges in developing robust and trustworthy AI systems. He explores foundation models that generalize across diverse clinical tasks, methods to ensure reliability and clinical acceptance, and how to translate algorithmic advances into efficient, deployable tools for healthcare and scientific discovery. chenyuyou+2

Work

Education

Projects

Writing

Uncovering Memorization Effect in the Presence of Spurious Correlations

June 1, 2025

Published in Nature Communications, this paper investigates the memorization effect in machine learning models, particularly when spurious correlations are present.

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Application of Large Language Models in Medicine

January 1, 2025

A comprehensive review of the applications of Large Language Models in the medical field, published in Nature Reviews Bioengineering.

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Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation

January 1, 2025

Published in IEEE Transactions on Medical Imaging, this work explores cross-modal conditioned reconstruction for medical image segmentation guided by language.

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Medical image registration via neural fields

June 1, 2024

A paper in Medical Image Analysis exploring medical image registration using neural fields.

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Segment Anything in Medical Images

January 1, 2024

A paper in Nature Communications that applies the 'Segment Anything' model to medical images, noted as an ESI - Top 1% highly cited paper.

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Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

January 1, 2024

Published in IEEE Transactions on Pattern Analysis and Machine Intelligence, this research revisits medical image segmentation under conditions of extremely limited labels, also an ESI - Top 1% highly cited paper.

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Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective

September 1, 2023

Published in NeurIPS 2023, this paper offers a variance-reduction perspective on semi-supervised medical image segmentation.

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