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Lab Members

Hard work always pays off 

Current Members

Assaf Ben-Kish

Assaf Ben-Kish

PhD on Large Multimodal Models

Erez Yosef

Erez Yosef

PhD on Computational Imaging

Moran Yanuka

Moran Yanuka

PhD on Large Multimodal Models

Mika Yagoda

Mika Yagoda

MSc on Deep Learning Robustness

Tal Rubinstein

Tal Rubinstein

MSc on State Space Models Robustness

Noga Bar

Noga Bar

PhD on Learning with small data

Nimrod Shabtay

Nimrod Shabtay

PhD on Large Multimodal Models

Lev Ayzenberg

Lev Ayzenberg

PhD on Medical Imaging, with Prof. Hayit Greenspan

Kohav Solomon

Kohav Solomon

MSc on State Space Models

Karine Eliashiv

Karine Eliashiv

MSc on MRI Imaging

Gal Metzer

Gal Metzer

PhD on Geometric Deep Learning, with Prof. Daniel Cohen-Or

Dana Cohen-Hochberg

Dana Cohen-Hochberg

PhD on Generative AI

Dana Weitzner

Dana Weitzner

PhD on Analysis of Neural Networks

Noa Kraicer

Noa Kraicer

MSc on Guided Lensless Imaging

Liav Hen

Liav Hen

MSc on Deep Learning for Metasurfaces

Aviad Dahan

Aviad Dahan

PhD on Generative AI, with Prof. Lior Wolf

Lior Shafir

Lior Shafir

PhD on Networking Security, with Avishai Wool

Rajaei Khateeb

Rajaei Khateeb

PhD on 3D deep learning

Netanel Nisan

Netanel Nisan

MSc on Deep Learning for Metasurfaces

Alumni

Postdocs:

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PhD Graduates: 

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  • Shady Abu Hussein, "Advancing Deep Learning by Integrating Signal Processing Techniques" (Jun. 2019 - Feb. 2025)

  • Amir Hertz, ``Towards Intuitive Creation, Modeling and Editingof Shapes and Images'' (Oct. 2018 - Sep. 2023), co-advisor: Daniel Cohen-Or

  • Amnon Drory, ``On The Robustness Of Neural Nets And Point Cloud Registration Algorithms'', (Apr. 2017 - Jan. 2023), co-advisor: Shai Avidan

  • Eli Schwartz, ``Adapting Computer Vision Models to Novel Distributions'', (Jun. 2019 - Dec. 2022), co-advisor: Alex M. Bronstein

  • Gilad Cohen, ``Attacks and Defenses for Deep Neural Networks'' (Feb. 2017 - Jun. 2022)

  • Shay Elmalem,  ``Joint Design of Optics and Image Processing Algorithms Using Deep Learning'' (Nov. 2016 - Dec. 2021), co-advisor: Emanuel Marom

  • Rana Hanocka, ``Deep Learning for Geometry Processing'' (Oct. 2017 - Jun. 2021), co-advisor: Daniel Cohen-Or. Now Assistant Professor at U. Chicago 

  • Tom Tirer, ``General Approaches for Solving Inverse Problems with Arbitrary Signal Models'' (Nov. 2016 - Nov. 2020). Now Assistant Professor at Bar-Ilan University 

  • Lihi Shiloh, ``Distributed Acoustic Sensing'' (Oct. 2017 - Apr. 2020), co-advisor: Avishay Eyal. Now at Microsoft

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MSc Graduates: 

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  • Moran Yanuka on ``ICC: Quantifying Image Caption Concreteness for Multimodal Dataset Curation'', (Oct. 2022 - Dec. 2024)

  • Aviad Dahan on ``Video Polyp Segmentation using Implicit Networks'', (Oct. 2022 - Aug. 2024), co-advisor: Lior Wolf

  • Yoav Kurtz on ``Group Orthogonalization Regularization for Vision Models Adaptation and Robustness'', (May. 2021 - Dec. 2023)

  • Nir Yellinek, ``3VL: Using Trees to Teach Vision \& Language Models Compositional Concepts'' (April. 2022 - Dec. 2023)

  • Shahaf Ettedgui, ``Boosting Semantic Segmentation using Progressive Cyclic Style-Transfer'' (Nov. 2021 - Jun. 2022)

  • Amit Henig, ``Utilizing Excess Resources in Training Neural Networks'' (Oct. 2019 - May. 2020)

  • Jonathan Shani, ``Denoiser-based projections for 2-D super-resolution multi-reference alignment'' (Nov. 2020 - May. 2022), co-advisor: Tamir Bendori

  • Meitar Shechter, ``Geometry-Aware Control Point Deformation'' (Nov. 2020 - Feb. 2022), co-advisor: Daniel Cohen-Or

  • Dana Cohen-Hochberg, ``Semi-supervised learning using styleGAN'' (April 2020 - Feb.2022), co-advisor: Hayit Greenspan

  • Noga Bar, ``Multiplicative Reweighting for Robust Neural Network Optimization'' (Jun. 2020 - Oct.2021), co-advisor: Tomer Koren

  • Gal Metzer, ``Point Clouds Consolidation and Normal Estimation for Surface Reconstruction'' (Oct. 2019 - Sep. 2021), co-advisor: Daniel Cohen-Or  

  • Guy Buchkin, ``Fine-grained Angular Contrastive Learning with Coarse Labels'' (Jun. 2020 - Jul. 2021)

  • Yuri Feigin, ``Generative Adversarial Encoder Learning'' (Jan. 2017 - Jun. 2021)

  • Einva Yogev, ``An Interpretation of Regularization by Denoising and its Usage with The Back-Projected Fidelity Term'' (Oct. 2018-Apr. 2021)

  • Avi Resler, ``Deep learning for archaeological information prediction and communities detection'' (Oct. 2018 - Oct. 2020), co-advisor: Filipe Natalio.  

  • Oshrat Bar, ``A spectral perspective of neural networks robustness to label noise'' (Oct. 2018 - Oct. 2020)

  • Sapir Kaplan, ``Self-supervised neural architecture search'' (Oct. 2018 - Nov. 2020)

  • Jenny Zukerman, ``BP-DIP: A Backprojection based Deep Image Prior'' (Oct. 2017 - Jul. 2020)

  • Yotam Gil, ``Using monocular depth estimation to improve stereo imaging'' (Aug. 2018 - Jun. 2020)

  • Tal Dimry, ``Best Buddies Registration For Point Clouds'', (Oct. 2019 - May. 2020), co-advisor: Shai Avidan

  • Tal Perl, ``Low Resource Sequence Tagging using Sentence Reconstruction'' (Dec. 2017 - Apr. 2020)

  • Roee Levy, ``Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data'' (Apr. 2018 - Jan. 2020)

  • Yoav Chai, ``Deep Global Mapping for Image Enhancement and Domain Adaptation'' (Oct. 2018 - Mar. 2020)

  • Sivan Doveh, ``Differentiable Efficient Generator Search'', (Oct. 2017 - Feb. 2020)

  • Daniel Brodeski, ``Deep Radar Detector'', (Jan. 2017 - Oct. 2019)

  • Dana Weitzner, ``Face Authentication from Grayscale Coded Light Field'' (Nov. 2017 - Sep. 2019), co-advisor: David Mendelovic

  • Daniel Jakubovitz, ``Robustness in Deep Learning'' (Jan. 2018 - May. 2019)

  • Shachar Ben Dayan, ``Implementation of Light-Field Re-Focusing with Sparse Angular Information Using Neural Networks'' (Oct. 2017 - Apr. 2019), co-advisor: David Mendelovic

  • Hillel Sreter, ``Approximate Convolutional Sparse Coding'' (Apr. 2017- Apr. 2019)

  • Dor Bank, ``the Relationship between Dropout and Equiangular Tight Frames'' (Apr. 2017 - Mar. 2019)

  • Ofir Nabati, ``compressed light field reconstruction using neural networks'' (Oct. 2017 - Jan. 2019), co-advisor: David Mendelovic

  • Tal Levy, ``Rankding recovery from limited comparisons using low-rank matrix completion'' (Feb. 2017 - Sep. 2019)

  • Eli Schwartz, ``End-to-End Learning of the Full Image Processing Pipeline'' (Mar. 2016 - Jun. 2018), co-advisor: Alex M. Bronstein

  • Guy Leibovitz, ``Efficient least residual greedy algorithms for sparse recovery'', (Mar. 2016 - Feb. 2018)

  • Elad Plaut, ``A greedy approach to convolutional sparse coding'', (Feb. 2017 - Feb. 2018)

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