Neurocomputation and machine learning of the retina
My laboratory’s research interest is in applying machine learning analysis towards understanding the human retina through single-cell transcriptomics analysis. Our research 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. We are implementing an approach that utilizes single nuclei expression data and a new field of machine learning called 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. Our research is high risk; however, it is also high reward as it has the potential to transform human health and an understanding of retinal function.