
Lab Members
Hard work always pays off
Current Members
Erez Yosef, PhD
Deep optics and computational imaging
Shady Abu-Hussein, PhD
Solving inverse problems using deep learning
Amnon Drory, PhD
Shape registration, Co-advised with Shai Avidan

Moran Yanuka, MSc
Self-supervised 3D learning

Lev Ayzenberg, MSc
Self-supervised leraning for medical data, Co-advised with Hayit Greenspan

Noga Bar, PhD
Analysis of neural network weights and structure

Dana Cohen-Hochberg
Generative models
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Yoav Kurtz, MSc
Optimization in deep learning

Aviad Dahan, MSc
Video generation and editing, Co-advised with Daniel Cohen-Or

Dana Cohen, MSc
3D data generation, Co-advised with Daniel Cohen-Or
Dana Weitzner, PhD
Low-rank models for inverse problems and deep learning
Geometric deep learning, Co-advised with Daniel Cohen-Or

Mika Yaogda, MSc
Generative models

Nir Yellinek, MSc
Interpretable deep learning
Amir Hertz, PhD
3D shape editing, Co-advised with Daniel Cohen-Or
Eli Schwartz, PhD
Few shot learning for classification and regression, Co-advised with Alex Bronstein

David Uliel, MSc
Information theory for domain adaptation & transfer learning

Nimrod Shabtay, MSc
Learning from a single image
Alumni
Postdocs:
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Tom Tirer (Nov. 2020-Nov. 2021)
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Jasmeet Singh (2020), Now Professor at Thapar University
PhD Graduates:
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Gilad Cohen, ``Attacks and Defenses for Deep Neural Networks'' (Feb. 2017 - Jun. 2022)
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Shay Elmalem, ``Joint Design of Optics and Image Processing Algorithms Using Deep Learning'' (Nov. 2016 - Dec. 2021), co-advisor: Emanuel Marom
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Rana Hanocka, ``Deep Learning for Geometry Processing'' (Oct. 2017 - Jun. 2021), co-advisor: Daniel Cohen-Or. Now Assistant Professor at U. Chicago
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Tom Tirer, ``General Approaches for Solving Inverse Problems with Arbitrary Signal Models'' (Nov. 2016 - Nov. 2020). Now Assistant Professor at Bar-Ilan University
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Lihi Shiloh, ``Distributed Acoustic Sensing'' (Oct. 2017 - Apr. 2020), co-advisor: Avishay Eyal. Now at Microsoft
MSc Graduates:
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Shahaf Ettedgui, ``Boosting Semantic Segmentation using Progressive Cyclic Style-Transfer'' (Nov. 2021 - Jun. 2022)
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Amit Henig, ``Utilizing Excess Resources in Training Neural Networks'' (Oct. 2019 - May. 2020)
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Jonathan Shani, ``Denoiser-based projections for 2-D super-resolution multi-reference alignment'' (Nov. 2020 - May. 2022), co-advisor: Tamir Bendori
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Meitar Shechter, ``Geometry-Aware Control Point Deformation'' (Nov. 2020 - Feb. 2022), co-advisor: Daniel Cohen-Or
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Dana Cohen-Hochberg, ``Semi-supervised learning using styleGAN'' (April 2020 - Feb.2022), co-advisor: Hayit Greenspan
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Noga Bar, ``Multiplicative Reweighting for Robust Neural Network Optimization'' (Jun. 2020 - Oct.2021), co-advisor: Tomer Koren
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Gal Metzer, ``Point Clouds Consolidation and Normal Estimation for Surface Reconstruction'' (Oct. 2019 - Sep. 2021), co-advisor: Daniel Cohen-Or
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Guy Buchkin, ``Fine-grained Angular Contrastive Learning with Coarse Labels'' (Jun. 2020 - Jul. 2021)
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Yuri Feigin, ``Generative Adversarial Encoder Learning'' (Jan. 2017 - Jun. 2021)
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Einva Yogev, ``An Interpretation of Regularization by Denoising and its Usage with The Back-Projected Fidelity Term'' (Oct. 2018-Apr. 2021)
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Avi Resler, ``Deep learning for archaeological information prediction and communities detection'' (Oct. 2018 - Oct. 2020), co-advisor: Filipe Natalio.
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Oshrat Bar, ``A spectral perspective of neural networks robustness to label noise'' (Oct. 2018 - Oct. 2020)
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Sapir Kaplan, ``Self-supervised neural architecture search'' (Oct. 2018 - Nov. 2020)
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Jenny Zukerman, ``BP-DIP: A Backprojection based Deep Image Prior'' (Oct. 2017 - Jul. 2020)
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Yotam Gil, ``Using monocular depth estimation to improve stereo imaging'' (Aug. 2018 - Jun. 2020)
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Tal Dimry, ``Best Buddies Registration For Point Clouds'', (Oct. 2019 - May. 2020), co-advisor: Shai Avidan
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Tal Perl, ``Low Resource Sequence Tagging using Sentence Reconstruction'' (Dec. 2017 - Apr. 2020)
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Roee Levy, ``Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data'' (Apr. 2018 - Jan. 2020)
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Yoav Chai, ``Deep Global Mapping for Image Enhancement and Domain Adaptation'' (Oct. 2018 - Mar. 2020)
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Sivan Doveh, ``Differentiable Efficient Generator Search'', (Oct. 2017 - Feb. 2020)
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Daniel Brodeski, ``Deep Radar Detector'', (Jan. 2017 - Oct. 2019)
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Dana Weitzner, ``Face Authentication from Grayscale Coded Light Field'' (Nov. 2017 - Sep. 2019), co-advisor: David Mendelovic
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Daniel Jakubovitz, ``Robustness in Deep Learning'' (Jan. 2018 - May. 2019)
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Shachar Ben Dayan, ``Implementation of Light-Field Re-Focusing with Sparse Angular Information Using Neural Networks'' (Oct. 2017 - Apr. 2019), co-advisor: David Mendelovic
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Hillel Sreter, ``Approximate Convolutional Sparse Coding'' (Apr. 2017- Apr. 2019)
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Dor Bank, ``the Relationship between Dropout and Equiangular Tight Frames'' (Apr. 2017 - Mar. 2019)
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Ofir Nabati, ``compressed light field reconstruction using neural networks'' (Oct. 2017 - Jan. 2019), co-advisor: David Mendelovic
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Tal Levy, ``Rankding recovery from limited comparisons using low-rank matrix completion'' (Feb. 2017 - Sep. 2019)
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Eli Schwartz, ``End-to-End Learning of the Full Image Processing Pipeline'' (Mar. 2016 - Jun. 2018), co-advisor: Alex M. Bronstein
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Guy Leibovitz, ``Efficient least residual greedy algorithms for sparse recovery'', (Mar. 2016 - Feb. 2018)
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Elad Plaut, ``A greedy approach to convolutional sparse coding'', (Feb. 2017 - Feb. 2018)