Spectral graph theory is the interplay between linear algebra and combinatorial graphs. Laplace’s equation and its discrete form, the Laplacian matrix, are becoming increasingly popular in optimization, image processing, and machine learning. This talk will discuss some key ideas in this Laplacian paradigm for designing efficient algorithms. They build upon tools from numerical analysis, graph algorithms, and random matrix theory.