Bahjat Kawar

Ph.D. in computer science from Technion. Computer vision researcher at Apple.
Researching machine learning & computer vision. Interested in diffusion models.
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About Me

I am a computer vision researcher at Apple in Herzliya. I recently graduated with a Ph.D. in computer science from Technion, researching computer vision and machine learning under the supervision of Prof. Michael Elad. I am interested in inverse problems, generative models, representation learning, and diffusion models. I have also spent the 2022 summer as a research intern at Google Research in Tel Aviv.

Prior to my Ph.D. studies, I obtained my B.Sc. in computer science from Technion, summa cum laude, as the class valedictorian. I also worked as a research intern at Cornell Tech in New York City, and as a freelance web and app developer.



Bahjat Kawar*, Noam Elata*, Tomer Michaeli, Michael Elad
GSURE-Based Diffusion Model Training with Corrupted Data
TMLR 2024, in Transactions on Machine Learning Research.

Noam Elata, Bahjat Kawar, Tomer Michaeli, Michael Elad
Nested Diffusion Processes for Anytime Image Generation
WACV 2024, in The IEEE/CVF Winter Conference on Applications of Computer Vision.


Hadas Orgad*, Bahjat Kawar*, Yonatan Belinkov
Editing Implicit Assumptions in Text-to-Image Diffusion Models
ICCV 2023, in The IEEE/CVF International Conference on Computer Vision.
[website]  [code]

Roy Ganz, Bahjat Kawar, Michael Elad
Do Perceptually Aligned Gradients Imply Adversarial Robustness?
ICML 2023 oral presentation, in International Conference on Machine Learning.

Bahjat Kawar*, Shiran Zada*, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, Michal Irani
Imagic: Text-Based Real Image Editing with Diffusion Models
CVPR 2023, in The IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Bahjat Kawar, Roy Ganz, Michael Elad
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance
TMLR 2023, in Transactions on Machine Learning Research.

Michael Elad, Bahjat Kawar, Gregory Vaksman
Image Denoising: The Deep Learning Revolution and Beyond —A Survey Paper—
SIIMS 2023, in SIAM Journal on Imaging Sciences.


Bahjat Kawar*, Jiaming Song*, Stefano Ermon, Michael Elad
JPEG Artifact Correction using Denoising Diffusion Restoration Models
NeurIPSW 2022, in NeurIPS Workshop on Score-Based Methods.

Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song
Denoising Diffusion Restoration Models
NeurIPS 2022, in Advances in Neural Information Processing Systems.
[website]  [code]

Tsachi Blau, Roy Ganz, Bahjat Kawar, Alex Bronstein, Michael Elad
Threat Model-Agnostic Adversarial Defense using Diffusion Models
Preprint, arXiv:2207.08089.


Bahjat Kawar, Gregory Vaksman, Michael Elad
SNIPS: Solving Noisy Inverse Problems Stochastically
NeurIPS 2021, in Advances in Neural Information Processing Systems.

Bahjat Kawar, Gregory Vaksman, Michael Elad
Stochastic Image Denoising by Sampling from the Posterior Distribution
ICCVW 2021, in ICCV Workshop on Advances in Image Manipulation.


I am a recipient of the following awards:
  • Technion MLIS-TCE Best Paper Award 2022
  • Council for Higher Education VATAT Scholarship 2021
  • Apple Excellence Award 2019
  • Ernest Freudman Fellowship 2018
  • LAPIDIM Excellence Program 2018-2020
  • Technion President's List Award for Excellence (6×) 2017-2020
I have served as a reviewer / program committee member at the following venues:
  • NeurIPS 2021, 2022, 2023
  • ICML 2022
  • AAAI 2023
  • CVPR 2023
  • ICCV 2023
  • TMLR 2023
  • Workshops at ICCV 2021, ICLR 2022, ICML 2022, ICML 2023
  • IEEE Transactions on Computational Imaging 2022
I was also a co-organizer of the NeurIPS 2023 Workshop on Diffusion Models.


I teach / have taught the following courses as a TA:
  • Generative AI - Diffusion Models (236610) – Winter 2022-2023
  • Project in Image Processing (234329) – Winter 2021-2022
  • Operating Systems (234123) – Spring 2020, Winter 2020-2021, Spring 2021
  • Logic Design (234262) – Winter 2018-2019