Michael Rabinovich
Geometry processing researcher focused on discrete differential geometry
Zurich, Switzerland
Summary
Expert in geometry processing and discrete differential geometry, with a research focus on efficient shape modeling algorithms and developable surface representations. github+1
Award-winning academic work: PhD dissertation on modeling developable surfaces earned the ETH Medal, reflecting both theoretical contributions and practical relevance to fabrication and architectural geometry. ethz+1
Transitioned from academia to industry research and leadership roles (CommonGround-AI, Microsoft, Nfinite), indicating applied research experience and technical leadership in advanced technologies. github+1
Contributed several influential publications and projects (e.g., SLIM, discrete geodesic nets) that are widely cited in geometry processing and computational design communities. nyu+2
Work
Education
Projects
Writing
Modeling Developable Surfaces with Discrete Orthogonal Geodesic Nets (PhD thesis)
January 1, 2020Doctoral dissertation introducing a discrete model for inextensible thin sheets, proving mathematical properties and demonstrating applications in 3D modeling and fabrication.
Discrete Geodesic Nets for Modeling Developable Surfaces
January 1, 2018ACM Transactions on Graphics paper presenting discrete geodesic nets as a method for modeling developable surfaces.
Scalable Locally Injective Mappings
January 1, 2017Paper on scalable approaches to locally injective mappings for geometry processing and mesh parameterization.
Least-squares rigid motion using SVD (technical note)
January 1, 2017Technical note on computing least-squares rigid motion using singular value decomposition (co-authored with Olga Sorkine-Hornung).