Emil Saucan, Braude College of Engineering

Abstract

We present the adaptations of Forman's discretization of Ricci curvature to the setting of networks and their higher dimensional generalizations and we explore their applications to a variety of real-life applications, such as: brain networks, chemical reactions, financial market crashes, stem cells and cancer research, autism understanding, intelligence of communication and social networks, deep learning, texture understanding, psychology and semantics. We also show how it naturally allows for the understanding of the long-time evolution of networks, their sampling as well as their study through persistent homology.

See: https://theory.cs.rutgers.edu/theory_seminar