Application-Driven Geometric Machine Learning
Speaker: Justin Solomon
Justin Solomon is an associate professor of Electrical Engineering and Computer Science in the MIT Computer Science and Artificial Intelligence Laboratory. He runs the MIT Geometric Data Processing group, which studies problems at the intersection of geometry, large-scale optimization, and applications in machine learning, graphics, and vision.
From 3D modeling to autonomous driving, a variety of applications can benefit from data-driven reasoning about geometric problems. The available data and preferred shape representation, however, varies widely from one application to the next. Indeed, the one commonality among most of these settings is that they are not easily approached using data-driven methods that have become de rigueur in other branches of computer vision and machine learning. In this talk, I will summarize recent efforts in my group to develop learning architectures and methodologies paired to specific applications, from point cloud processing to mesh and implicit surface modeling. In each case, we will see how mathematical structures and application-specific demands drive our design of the learning methodology, rather than bending application demands or ignoring geometric details to apply a standard data analysis technique.