Neurocomputation and machine learning of the retina
Brian Hafler’s lab is focused on applying machine learning to understand the human retina through single-cell transcriptomics. The research group is tackling this problem by combining novel computational tools and machine learning to provide an unparalleled depth of insight into key pathways underlying retinal homeostasis and disease in macular degeneration. They are implementing an approach that uses single-nuclei expression data and manifold learning. This integrative approach offers an advantage over traditional approaches as it allows data integration of rare cellular populations on a scale that was not previously possible. Their research is high-risk; however, it is also high-reward, as it has the potential to transform human health and our understanding of retinal function.