Geatan De Waele defends his PhD Thesis titled Self-supervised Transformers for Biological Data Modalities

On Friday, September 19th, Gaetan De Waele successfully defended his PhD thesis titled “Self-supervised Transformers for Biological Data Modalities”. His work focused on adapting transformer architectures to various biological data types, with a particular emphasis on leveraging self-supervised learning techniques. Gaetan’s research addressed two main areas: (1) incorporating symmetries and geometric properties inherent in biological data into model design, and (2) developing self-supervised learning approaches to enhance model performance. We congratulate Gaetan on this significant achievement and wish him the best in his future endeavors!

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