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Since September 2022 Im Head of EMEA and AI at Tulip where we transform augment the manufacturing industry with our no-code platform. Prior to that I was the CEO and Founder at BeyondMinds where I led a group of top AI researchers and engineers to build the next stage of AI operation platform. Prior to joining BeyondMinds I did my PhD at the Technion-Israel, where I worked with Prof. Lihi Zelnik-Manor. My work lies at the intersection of computer vision and deep learning. Specifically, my research interests are in realistic image generation, manipulation, and transformation, focussing on tools, algorithms, and new paradigms for photo editing and synthesis. During my PhD research I was worked on Image-to-image problems, Image Generation and GANs, Super-resolution, Photorealistic synthesis, and Template Matching. My masters and bachelors were done at Tel Aviv University in Biomedical Engineering (cum laude).
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Podcast: Applied AI in Manufacturing - Startup Blueprint Podcast 
Podcast: Tourists in AI-Land
 
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We propose Self-Supervised Dynamic Networks (SSDN): an input-dependent mechanism, inspired by dynamic networks, that allows a self-supervised network to predict the weights of the main network, and thus directly handle covariate shifts at test-time |
 
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The goal of this paper is to manipulating images in order to control the saliency of objects. We propose an approach that considers the internal color and saliency properties of the image. |
 
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We present a first attempt at alleviating the need for re-training. Rather than fixing the network at training time, we train a ``Dynamic-Net'' that can be modified at inference time. |
 
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We propose a novel method that makes an explicit use of the discriminator in test-time, in a feedback manner in order to improve the generator results. |
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We suggest the synthesis of new data by mixing the class specified and unspecified representation of different factors in the training data. |
 
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We use the contextual loss for two image restoration problems: super-resolution and normal estimation and reveal its relation to the KL-divergence. |
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In contrast to previous SR challenges, our evaluation methodology jointly quantifies accuracy and perceptual quality |
 
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We suggest a new loss function for image manipulation and generation. We show some very cool applications such as semantic style transfer and single image animations. |
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Manipulating images in order to control the saliency of objects is the goal of this paper. We propose an approach that considers the internal color and saliency properties of the image. It changes the saliency map via an optimization framework that relies on patch-based manipulation using only patches from within the same image to maintain its appearance characteristics. |
 
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Using the Screened Poisson Equation for photorealism style transfer |
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Novel measure for template matching named Deformable Diversity Similarity -- based on the diversity of feature matches between a target image window and the template |
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Controlling the Latent Space at Inference Time -- Israel Vision Day 2018
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