Research

Research Interest

My research interests encompass graph signal processing, graph neural networks, geometric data analysis, wireless communications, and scalable autonomous systems. My research builds the mathematical foundations to understand the fundamental properties of graph neural networks via a manifold perspective. These theoretical understandings can advance scalable and stable solutions in many graph-structured applications such as communication networks, robotic systems, and data manifolds.

My ultimate goal is to use insights from signal processing to characterize mathematical foundations, develop novel architectures, and devise alternative training procedures for deep learning over real-world geometric data.