[cosc-grad-students-list] REMINDER: Presentation/Open Discussion – with candidate Dr. Yuzhi Guo

[Eulenfeld, Menda]

Updated meeting room.

CV attached.

Title: Pre-training AI Foundation Models for Biomedical Science

Abstract: Pre-training models have shown a strong ability to learn generalizable representations by leveraging large amounts of unlabeled or weakly labeled data. However, compared to generic or natural data, biological data are often more complex, heterogeneous, and available at a much smaller scale, which introduces additional challenges. In this talk, I will present my research on developing self-supervised learning algorithms and foundation models that explicitly address these challenges by leveraging biological priors such as evolutionary information, geometric structure learning, and effective training of large foundation models under limited biomedical data settings. The talk will cover two main directions: self-supervised learning frameworks for protein representations that improve a wide range of downstream protein structure property prediction tasks, and foundation models for biomedical image processing.

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