Faculty of Informatics
Via Giuseppe Buffi 13
Michael Bronstein is an assistant professor in the Institute of Computational Science, Faculty of Informatics, USI. He received the B.Sc. summa cum laude (2002) from the Department of Electrical Engineering and Ph.D. with distinction (2007) from the Department of Computer Science, Technion (Israel Institute of Technology). Prior to joining USI, he held a visiting appointment at Stanford university. His main research interests are theoretical and computational methods in metric geometry and their application to problems in computer vision, pattern recognition, shape analysis, computer graphics, image processing, and machine learning.
Prof. Bronstein has authored over 70 publications in leading journals and conferences, over a dozen of patents and the book "Numerical geometry of non-rigid shapes" (published by Springer Verlag). His research was recognized by numerous awards and was featured in CNN, SIAM News, and Wired. Michael Bronstein was the co-chair of the Workshop on Non-rigid shapes and deformable image alignment (NORDIA) in 2008-2011 and has served on review and program committees of major conferences in computer vision and pattern recognition.
Besides academic work, Dr. Bronstein is actively involved in industrial applications, technology transfer and commercialization, and consulting to technological companies in the computer vision, image processing, and pattern recognition domain, both in technical and management positions. 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 video 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 acquisition of Invision by Intel in 2012, Michael Bronstein currently serves as advisor and research scientist at Intel.
Geometry: theoretical and computational methods in metric geometry, embedding problems, discrete Gromov-Hausdorff distances, spectral and diffusion geometry, geometric approaches to problems in image sciences.
Computer graphics and shape analysis: non-rigid similarity and correspondence, partial similarity, symmetry, feature based methods, large-scale shape retrieval, invariant texture mapping, shape synthesis and morphing.
Computer vision: 3D acquisition and reconstruction, feature descriptors, image and video retrieval and search, copy detection, use of bioinformatics algorithms for video analysis, Internet-scale applications.
Machine learning: manifold learning, non-linear dimensionality reduction, multidimensional scaling, metric learning, similarity-sensitive hashing, multimodal data fusion, multimodal metric learning.
Biometrics: 3D face recognition.