This research projec aims to contribute to the development of methods that extract informative and interpretable features from high-dimensional datasets, with a focus on uncovering high-level causally related factors that describe meaningful semantics of the data. This, in turn, can help us gain deeper insights into the representations found within advanced generative models, particularly foundation models and LLMs, with the goal of improving their efficiency and safety.

Amongst the leading causes of retinal dystrophies worldwide is Retinitis pigmentosa, a severe disease often caused by splice variants in the USH2A gene. This project aims to develop a safe CRISPR-based therapeutic strategy for correction of such splicing defects. Using enhanced-deletion nucleases, the disease-causing alterations can be eliminated, hereby restoring correct protein synthesis. The focus lies on safety features as well as the development of an inducible viral delivery system for clinical application.