Danai Brilli

This PhD project aims to develop scalable AI foundation models for large-scale transcriptomics data, with applications in disease modeling and precision treatment optimization. The initial focus will be on building robust neural representations of gene expression across a wide range of healthy and diseased tissues using public datasets. By leveraging large neural models the project will learn data-driven, multi-modal embeddings that unify transcriptomic, genomic, and perturbation information.

The long-term goal is to move toward an Al-powered "virtual cell" capable of modeling molecular dynamics and guiding personalized treatment strategies. This work bridges foundational modeling with real-world biomedical impact and contributes to the advancement of precision medicine, pharmacogenomics, and systems biology.