Assistant Adjunct Professor 

UCLA, Department of Mathematics

520 Portola Plaza,

7073 Math Science Building

Los Angeles, CA 90095

I am an Assistant Adjunct Professor in the UCLA Department of Mathematics, working with Professor Andrea Bertozzi. I am also a member of the UCLA Computer Vision Labworking with Professor. Stefano Soatto.  

I finished my PhD  in the Computer Science Department at the University of Southern California in 2016. My thesis advisor was Professor Gerard Medioni with whom I was working on developing a global framework for Tensor Voting, and on Manifold Learning. I also collaborated with Professor Antonio Ortega on developing a new framework for Manifold Denoising using tools from Graph Signal Processing.

I hold B.Sc. in Mathematics from the Technion, Israel Institute of Technology and M.Sc. in Applied Mathematics from Tel Aviv University. My Master’s thesis advisors were Professor Amir Averbuch and Professor Shai Dekel. My M.Sc. research focused primarily on an alternative method for simultaneous image acquisition and compression called Adaptive Compressed Sensing. The Adaptive Compressed Sensing framework replaces the universal acquisition of incoherent measurements used in the 'classic Compressed Sensing' framework with a fast and direct method for adaptive wavelet tree acquisition. The main advantages of this adaptive approach are that the decoding time is significantly faster and that it allows greater control over the compressed images quality, in particular, the sharpness of edge.

Research Interests 

My main research interests are in the areas of Multi-scale representations, Manifold Learning and Tensor Voting. In particular I am interested in developing robust mathematical formulations to create efficient algorithms in Image Processing, Computer Vision and Artificial Intelligence applications.


S. Deutsch, S. KolouroK. KynugnamY. Owechko, and S. Soatto, “Zero Shot Learning Via Multi-Scale Manifold Regularization”, to apear in CVPR 2017. 

S. Deutsch, A. Ortega, and G. Medioni. “Graph Manifold Based Frequency Analysis For Denoising",  Submitted.

S. Deutsch and G. Medioni. "Learning the Geometric Structure of Manifolds with Singularities Using the Tensor Voting Graph", To appear in Journal of Mathematical Imaging and Vision.

S. Deutsch, A. Ortega, and G. Medioni. “Manifold Denoising Based on Spectral Graph Wavelets",  ICASSP 2016.

S. Deutsch and G. Medioni. “Intersecting Manifolds: Detection, Segmentation, and Labeling”,  International Joint Conference on Artificial Intelligence,  (IJCAI), 2015. [pdf]

S. Deutsch and G. Medioni. “Unsupervised Learning Using the Tensor Voting Graph”, Scale Space and Variational Methods in Computer Vision (SSVM), 2015. [pdf]

A. Averbuch, S. Dekel and S.Deutsch. “Adaptive Compressed Image Sensing Using Dictionaries”. Siam Journal Of Imaging Sciences, 5(1), (2012), 57-89.[pdf]

S. Deutsch, A. Averbuch and S. Dekel. “Adaptive compressed image sensing based on wavelet modeling and direct sampling.”, Sampling Theory and Applications (SAMPTA) Conference, May 2009. Marseille, France.[pdf]


Spring 2017: MATH 151A - Applied Numerical Methods (I)

Winter 2017: MATH 151A - Applied Numerical Methods (I)