In recent years, geometric data is gaining increasing interest both in the academia and industry. In computer graphics and vision, this interest is owed to the rapid development of 3D acquisition and printing technologies, as well as the explosive growth of publicly-available 3D shape repositories. In machine learning, there is a gradual understanding that geometric structure plays an important role in high-dimensional complicated datasets.
In this talk, I will use the problem of manifold correspondence (a fundamental and notoriously hard problem with a wide range of applications in geometric processing, graphics, vision, and learning) as a showcase for classical methods from the domain of signal processing (such as sparse coding, joint diagonalization, and matrix completion) applied to geometric problems.
Michael Bronstein received the Ph.D. with distinction from the Department of Computer Science, Technion in 2007. In 2010, he joined the Institute of Computational Science in the Faculty of Informatics at the University of Lugano (USI), Switzerland. Prior to joining USI, Michael held a visiting appointment at Stanford university. His main research interests are theoretical and computational methods in spectral and metric geometry and their application to problems in computer vision, pattern recognition, shape analysis, computer graphics, image processing, and machine learning. Michael has authored over 70 publications in leading journals and conferences, over 20 patents, and the book "Numerical geometry of non-rigid shapes" (Springer, 2008). Highlights of his research were featured in CNN, SIAM News, and Wired. In 2012, he won the highly competitive ERC Starting Grant. In 2014, he participated as a Young Scientist in the World Economic Forum New Champions Meeting in China, an honor bestowed on 40 world's leading scientists below the age 40.
Besides academic work, Michael is actively involved in the hi-tech industry. His track record includes developing and licensing algorithms for large-scale video analysis applications at the Silicon Valley start-up company Novafora (2004-2009 as co-founder and VP of technology) and developing coded-light 3D camera based on his patents at the Israeli start-up Invision (2009-2012 as one of the principal technologists). Following the multi-million acquisition of Invision by Intel in 2012, Prof. Bronstein currently also serves as Research Scientist at Intel and is one of the key algorithm developers behind the recently announced Intel RealSense 3D camera.