### Shay Deutsch

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

**Contact Info:**

University of California, Los Angeles (UCLA), Department of Mathematics

Mathematical Sciences Building, room 5338

520 Portola Plaza, Los Angeles, CA 90095.

**Email:** shaydeu@math.ucla.edu

**Research Interests**

My research is primarily related to multi-scale representation and robust statistical estimation applied to developing graph based methods for unsupervised learning. I currently focus on problems that are in the union of network analysis and graph signal processing, and their application to zero shot learning and computational biology.

**Education**

I completed my PhD in the Computer Science Department at the University of Southern California. 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.

**Publications**

Jeffrey N. Law, Kyle Akers, Nure Tasnina, Catherine M. Della-Santina, Shay Deutsch, Meghana Kshirsagar, Judith Klein-Seetharaman, Mark Crovella, Padmavathy Rajagopalan, Simon Kasif, T. M. Murali (2021), *"Interpretable Network Propagation with Application to Expanding the Repertoire of Human Proteins that Interact with SARS-CoV-2*". In *GigaScience*.

S. Deutsch, S. Soatto, "*Graph Sylvester Embeddings for Network Analysis*", submitted.

S. Deutsch, S. Soatto. "*Spectral Embedding of Graph Networks*", preprint, [**pdf**].

S. Deutsch, A. Bertozzi and S. Soatto,"*Zero Shot Learning Using the Isoperimetric Loss*", AAAI-20, **[****pdf****].**

S. Deutsch, I. Masi and S. Soatto, "*Finding Structure in Point Cloud Data with the Robust Isoperimetric Loss*", Scale Space and Variational Methods in Computer Vision (SSVM19) **[pdf]**

S. Deutsch, A.Ortega and G.Medioni, "*Robust Denoising of Piece-Wise Smooth Manifolds*", ICASSP 2018, **[****pdf****].**

S. Deutsch, S. Kolouro, K. Kynugnam, Y. Owechko, and S. Soatto, “*Zero Shot Learning Via Multi-Scale Manifold Regularization*”, CVPR 2017, pp. 7112-7119,** [****pdf****].**

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",** ***Journal of Mathematical Imaging and Vision** 2017, 402-422.

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, 2009 **[pdf]**.

**Teaching**

**Fall ****2021: MATH 151A - Applied Numerical Methods (I)**

**Spring 2021: MATH 151B - Applied Numerical Methods (II)**

**Spring 2021: MATH 115A - Linear Algebra**

**Winter 2021: MATH 151A - Applied Numerical Methods (I)**

**Fall 2020: MATH 131A - ****Real Analysis**

**Summer 2020: MATH 151B - ****Applied Numerical Methods (II)**

**Spring 2020: MATH 151B - ****Applied Numerical Methods (II)**

**Winter 2020: MATH 151B - ****Applied Numerical Methods (II)**

**Spring 2019: MATH 151B - ****Applied Numerical Methods (II)**

**Fall 2018: MATH 151B - ****Applied Numerical Methods (II)**

**Spring 2018: MATH 151B - ****Applied Numerical Methods (II)**

**Winter 2018: MATH 170A - Probability Theory **

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

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