Summer School on Missing Data, Augmentation, and Generative Models

Missing data is a common problem in image processing and in general AI based methods. The source can be, for example, occlusions in 3D computer vision problems, poorly dyed tissue in biological applications, missing data points in long-term observations, or perhaps there is just too little annotated data for a deep-learning model to properly converge. […]

Data Science Methods for Disordered Materials

Aalborg University, Aalborg Fredrik Bajers Vej 7K, Aalborg, Denmark

Disordered materials, such as inorganic glasses, polymer glasses, cement hydrates, amorphous membrane, metal-organic framework glasses, and gels, are critical to our sustainable future. Their design has traditionally been done using the time-consuming and inefficient “trial-and-error” approach. However, new approaches relying on artificial intelligence, machine learning, and topological data analysis offer opportunities for accelerating the discovery […]