Raif Rustamov

raifrustamov at gmail dot com
personal page

Research interests

    My research interests lie in developing tools for processing of geometric data -- triangle meshes, volumes, point clouds, and weighted graphs -- arising in computer graphics, computer aided design, protein bioinformatics, medical imaging, and analysis of high-dimensional data sets. My current work focuses on the use of spectral methods and their extensions for processing of large collections of deformable surfaces and volumes. Another thread in my current research is constructing wavelets and other multiscale structures suitable for analyzing of and on geometric data.

Recent publications

J. Solomon, R. Rustamov, L. Guibas, and A. Butscher, Earth Mover’s Distances on Discrete Surfaces, Proc. SIGGRAPH (2014).   
J. Solomon, R. Rustamov, L. Guibas, and A. Butscher, Wasserstein Propagation for Semi-Supervised Learning, Proc. International Conference on Machine Learning (ICML 2014).   
N. Hu, R. Rustamov, and L. Guibas, Stable and Informative Spectral Signatures for Graph Matching, CVPR2014   
R. Rustamov, L. Guibas, Hyperalignment of Multi-Subject fMRI Data by Synchronized Projections, NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) 2013.   
Raif M. Rustamov and Leonidas Guibas, Wavelets on Graphs via Deep Learning, NIPS 2013   
Raif M. Rustamov, Maks Ovsjanikov, Omri Azencot, Mirela Ben-Chen, Frederic Chazal, and Leonidas Guibas. Map-based exploration of intrinsic shape differences and variability. ACM Trans. Graph. (Proc. SIGGRAPH) 32, 4, Article 72 (July 2013).   
Nan Hu, Raif M. Rustamov, and Leonidas Guibas, Graph Matching with Anchor Nodes: A Learning Approach, CVPR2013