I am interested in shapes as compact signal representations of objects from images, and their use in understanding and modeling biological morphology.
Although there have been several advances in developing analytical techniques for shape analysis, the ensuing statistical inference still uses linear signal processing approaches that rely on standard inner products on Euclidean spaces. With the increasing high resolution biomedical imaging acquisitions, it is becoming clear that this discrepancy in the approaches between statistical analysis and shape representation is a major limitation. Additionally with the acquisition of imaging modalities such as structural magnetic resonance imaging, or diffusion tensor imaging, we need novel approaches for signal representation and analysis. My interests lie in the development of computational methods for processing, analysis, and interpretation of the geometry of biomedical images and signals.