The field of gradual typing has grown exponentially over the past decade, both in terms of research and industrial adoption. Gradual typing, the idea of adding/strengthening types in existing programs, demands work on design, theory, implementation, and usability. As such, the field deserves a special journal issue with reflective papers.
Deep networks have led to new techniques for solving PDEs, particularly in high-dimensional settings, and the interpretation of some deep neural networks as nonlinear PDEs has led to a new frontier to gain theoretical insight and design new algorithms for deep learning. This special issue brings together new results at this new interface between applied mathematics and data science.