Image data science

Our laboratory develops computational predictive models for applications in pathology, radiology, systems biology, as well as mobile and remote sensing. Our mathematical models are based on hierarchical, multi-scale, physics and physiology inspired representation techniques and, in combination with statistical machine learning techniques, are used to regress structure-function relationships at the cellular, tissue, and organ scales. We’re particularly interested in designing low-level processing techniques (e.g. segmentation, registration, tracking), as well as high-level data analytics methods (e.g. detection, estimation, classification) to jointly mine signal, image, molecular, and genomic data to better understand differences between healthy versus diseased states.

Current projects include the development of mathematical signal and image transforms and hierarchical models for inference and predictive modeling. Applications currently being investigated include understanding cell function and morphology for cancer diagnosis and prognosis, brain morphology and function for analyzing healthy vs diseased populations, as well as detection, classification, and inverse problems in mobile and remote sensing.