On Geometry and Learning
Speaker: Ron Kimmel, Technion
Ron Kimmel is a Professor of Computer Science and Electrical & Computer Eng. (by courtesy) at the Technion where he holds the Montreal Chair in Sciences. He held a post-doctoral position at UC Berkeley and a visiting professorship at Stanford University. He has worked in various areas of shape reconstruction and analysis in computer vision, image processing, deep learning of big geometric data, and computer graphics. Kimmel’s interest in recent years has been understanding of machine learning, medical imaging and computational biometry, optimization of solvers to problems with a geometric flavor, and applications of metric, spectral, Riemannian, and differential geometries. Kimmel is an IEEE Fellow and SIAM Fellow for his contributions to image processing, shape reconstruction and geometric analysis. He is the founder of the Geometric Image Processing Lab. and a founder and advisor of several successful image processing and analysis companies.
Geometry means understanding in the sense that it involves finding the most basic invariants or Ockham’s razor explanation for a given phenomenon. At the other end, modern Machine Learning has little to do with explanation or interpretation of solutions to a given problem.
I’ll try to give some examples about the relation between learning and geometry, focusing on learning geometry, starting with the most basic notion of planar shape invariants, efficient distance computation on surfaces, and treating surfaces as metric spaces within a deep learning framework. I will introduce some links between these two seemingly orthogonal philosophical directions.