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.
Publications
2024
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Bahjat Kawar*, Noam Elata*, Tomer Michaeli, Michael Elad GSURE-Based Diffusion Model Training with Corrupted Data TMLR 2024, in Transactions on Machine Learning Research. [code] |
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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. [code] |
2023
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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] |
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Roy Ganz, Bahjat Kawar, Michael Elad Do Perceptually Aligned Gradients Imply Adversarial Robustness? ICML 2023 oral presentation, in International Conference on Machine Learning. |
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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. [website] |
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Bahjat Kawar, Roy Ganz, Michael Elad Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance TMLR 2023, in Transactions on Machine Learning Research. [code] |
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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. |
2022
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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. [code] |
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Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song Denoising Diffusion Restoration Models NeurIPS 2022, in Advances in Neural Information Processing Systems. [website] [code] |
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Tsachi Blau, Roy Ganz, Bahjat Kawar, Alex Bronstein, Michael Elad Threat Model-Agnostic Adversarial Defense using Diffusion Models Preprint, arXiv:2207.08089. |
2021
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Bahjat Kawar, Gregory Vaksman, Michael Elad SNIPS: Solving Noisy Inverse Problems Stochastically NeurIPS 2021, in Advances in Neural Information Processing Systems. [code] |
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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. |
Awards
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
- 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
Teaching
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