On Some Theory and Applications of Geometric Deep Learning

The objective of this thesis is two fold: 1) Understanding the theory of geometric deep learning through the lens of statistical learning theory, functional analysis, and other mathematical tools such as differential geometry and topology. 2) Apply geometric deep learning techniques in the field of 3D computer vision, reinforcement learning, molecular science, multimodal deep learning including foundation models/LLMs.