Robust Machine Learning for Biomedical Data: Efficiency, Reliability, and Generalizability
Chenyu You
IRB 4105 or https://umd.zoom.us/j/93666933047?pwd=gWgqOgGbBP6laZclyURdDG2mNdArBt.1
Thursday, April 30, 2026, 11:00 am-12:00 pm 
Abstract
Machine learning for healthcare is often developed on curated datasets, but deployed in settings where labels are scarce, classes are imbalanced, and data distributions shift across hospitals, patient populations, and imaging modalities. This gap raises a central question: how can we build learning methods that are data-efficient, reliable, and robust to the heterogeneity of real clinical data? In this talk, I will present my work on this question. I will begin with statistically grounded methods for learning from imperfect medical data, focusing on biomedical image analysis with limited annotations and long-tailed class distributions. I will then show how to build learning frameworks with formal guarantees, including methods for provably accurate anatomical modeling that incorporate domain structure directly into the learning process. Finally, I will present recent work on foundation models for biomedical imaging and on scalable predictive systems for clinical prediction under distribution shift. Together, these projects aim to make biomedical machine learning systems robust in real clinical settings where labels are scarce, data are heterogeneous, and distributions shift. .
Bio
Chenyu You is an Assistant Professor at Stony Brook University, with appointments in both the Department of Applied Mathematics and Statistics and the Department of Computer Science. He is also a core faculty member of the CVLab and the AI Institute and is affiliated with the Institute for Advanced Computational Science. His research focuses on fundamental and applied problems in computer vision and machine learning, with a focus on AI for health. He received his Ph.D. from Yale University in 2024, after earning an M.S. from Stanford University and a B.S. from Rensselaer Polytechnic Institute, all in electrical engineering. He has also spent time at Facebook AI Research (FAIR) and Google Research. He is active in the MICCAI community and serves as an associate editor for several journals, including IEEE Transactions on Medical Imaging and Medical Image Analysis. His recent honors include AAAI'26 New Faculty Highlights, CPAL'26 Rising Stars Award, ICML'25 Oral Presentation Award, EMBC'25 Top Paper Award, and IEEE TMI‘25 Distinguished Associate Editor Award. For more information, please check his website: https://chenyuyou.me/.
This talk is organized by Samuel Malede Zewdu