His research is focused on developing novel methods in Machine Learning and Computer Vision in the context of their applications in neuroimaging, neuroscience and healthcare. Specifically, he is interested in multi-resolution analysis of data that live in non-Euclidean spaces. This research thrust is motivated by the observation that a broad variety of statistical inference tasks on signals/functions can be made far more effective using a multi-resolution view of measurements. Many of his works are presented in top-tier AI conferences such as NIPS, CVPR, ICCV, ECCV as well as in Neuroimaging journals such as Neuroimage.