Geometric functional data analysis in biomedical imaging

We are working on approaches to build classification algorithms for imaging data from Ophthalmology to assess retinal changes in diabetic retinopathy. We are also working on leveraging the geometric functional data analysis approaches in conjunction with spatial analysis techniques for other medical imaging data such as radiological and histo-pathological imaging. The applications include imaging data from gliomas, pancreatic cancer and immunotherapy for lung cancer.

Radiogenomic analysis in gliomas

We are building Bayesian variable selection approaches to identify associations between molecular characteristics and imaging heterogeneity. These assocaitions are also studied while considering (by mimicking) the tumor growth process. We are specifically interested in radiological-imaging and the cancer driver-genes/pathways of lower grade gliomas. We are employing methods from geometric functional data analysis. We are also working on methods for compositional data to assess associations of genomics and the volumetric characteristics of the tumor.

Spatial modeling of large insurance claims and occurrence data

We are working on building efficient spatial risk-quantification models in insurance claims data. We are also building graphical Tweedie compound Poisson double generalized linear models.