Many problems in geometry processing, graph theory, and machine learning involve optimizations whose variables are defined over a geometric domain. The geometry of the domain gives rise to geometric structure in the optimization problem itself. In this talk, I will show how leveraging geometric structure in the optimization problem gives rise to efficient and stable algorithms applicable to a variety of application domains. In particular, I will describe new methods for problems arising in shape analysis/correspondence, flows on graphs, and surface parameterization.
Justin Solomon is an assistant professor in MIT's Department of Electrical Engineering and Computer Science. He leads the new Geometric Data Processing Group studying geometric problems in computer graphics, computer vision and machine learning.