Seminar Details

Over the past 30 years, we have been developing a general, scalable, computational learning and AI framework that combines principles of computational learning, neural nets, sparse methods, mixed norms, dictionary learning, and deformable modeling methods. This framework has been used for resolution of complex large scale problems in computer vision and biomedical image analysis. In computer vision we will present new machine learning methods for human behavior analytics and explainable scene understanding. In medical image analysis we will present segmentation, registration, tracking and disease classification methods based on clinical and preclinical data.