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Lab Members

Hard work always pays off 

Current Members

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Erez Yosef, PhD

Deep optics and computational imaging

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Shady Abu-Hussein, PhD

Solving inverse problems using deep learning

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Amnon Drory, PhD

Shape registration, Co-advised with Shai Avidan

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Moran Yanuka, MSc

Self-supervised 3D learning

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Lev Ayzenberg, MSc

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

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Noga Bar, PhD

Analysis of neural network weights and structure 

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Dana Cohen-Hochberg

Generative models

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Yoav Kurtz, MSc

Optimization in deep learning

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Aviad Dahan, MSc

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

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Dana Cohen, MSc

3D data generation, Co-advised with Daniel Cohen-Or

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Dana Weitzner, PhD

Low-rank models for inverse problems and deep learning

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Gal Metzer

Geometric deep learning, Co-advised with Daniel Cohen-Or

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Mika Yaogda, MSc

Generative models

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Nir Yellinek, MSc

Interpretable deep learning

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Amir Hertz, PhD

3D shape editing, Co-advised with Daniel Cohen-Or

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Eli Schwartz, PhD

Few shot learning for classification and regression, Co-advised with Alex Bronstein

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David Uliel, MSc

Information theory for domain adaptation & transfer learning

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Nimrod Shabtay, MSc

Learning from a single image

Alumni

Postdocs:

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PhD Graduates: 

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  • 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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  • 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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