Speaker:

Chenyu You

Title:

Robust Machine Learning for Biomedical Data: Efficiency, Reliability, and Generalizability

Abstract:


In the rapidly growing area of machine learning, there is profound promise in crafting intelligent, data-driven methods for diverse real-world applications. Yet, in safety-critical domains like healthcare, some fundamental challenges remain: (1) The insufficiency of raw biomedical data emphasizes the need for data-efficient and robust learning approaches. (2) The imperative of safety and stability necessitates a cohesive framework that unifies learning with theoretical guarantees. (3) The inherent heterogeneity and distribution shifts in real-world clinical data call for robust and generalizable learning methods.


As machine learning methods have become ubiquitous in clinical decision-making, their reliability and interpretability have become important. This is particularly crucial in the field of biomedical image analysis, where decision outcomes can have profound implications. I have developed novel machine-learning algorithms that enable provably accurate anatomical modeling with theoretical guarantees.


The development of medical foundation models often requires massive and diverse biomedical data. To this end, I have developed various foundation models for biomedical imaging data and explored novel applications of these models. I have also developed novel medical AI Agents that lead to scalable and accurate predictive modeling, particularly for distribution shift problems.



Biography:

Chenyu You is an Assistant Professor in the Department of Applied Mathematics & Statistics and the Department of Computer Science at Stony Brook University. He is also affiliated with the CVLab and AI institute. Previously, he received his Ph.D. in 2024 from Yale University under the advisement of James S. Duncan, his M.S. in 2019 from Stanford University under the advisement of Daniel Rubin, and his B.S. in 2017 from Rensselaer Polytechnic Institute under the advisement of Ge Wang, all in electrical engineering. He has also spent time at Facebook AI Research (FAIR), as well as Google Research.

His lab works on the principles and practice of machine intelligence, often with a focus on datasets, generalization, and making machine learning more reliable. Their applied research includes applications to healthcare, biomedical imaging, and cognitive neuroscience. See his publications and research topics for more details.